Geostatistical Analysis of Rainfall Spatial Variability and Interpolation Uncertainty in the Semi-Arid Triffa Plain (Northeast Morocco)

Geostatistical Analysis of Rainfall Spatial Variability and Interpolation Uncertainty in the Semi-Arid Triffa Plain (Northeast Morocco) | 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 Geostatistical Analysis of Rainfall Spatial Variability and Interpolation Uncertainty in the Semi-Arid Triffa Plain (Northeast Morocco) Mohammed LAABOUDI, Abdelhamid MEZRHAB, Zahar Elkheir ALIOUA, Wadii SNAIBI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8765693/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rainfall has shown significant variability during recent decades, particularly in semi-arid regions, where its spatial distribution strongly influences water availability and agricultural activities. In North Africa, rainfall remains irregular and unevenly distributed in space, making accurate spatial estimation essential for climate analysis and water resource management. The objective of this study is to assess rainfall spatial variability in the Triffa Plain (northeastern Morocco) and to identify the most reliable interpolation method under conditions of limited observational data. Annual rainfall data from nine stations covering the period 1992–2021 (30 years) were first subjected to a pre-processing phase, including normality, homogeneity, and break detection tests. The results indicate that all rainfall series follow a normal distribution and show no significant breaks at confidence levels ranging from 90% to 99%, confirming the homogeneity and reliability of the dataset. Two commonly used spatial interpolation techniques were then applied and compared: the deterministic Inverse Distance Weighting (IDW) method and the geostatistical Ordinary Kriging approach. Cross-validation was used to evaluate the performance of both methods. The results reveal that mean annual rainfall over the Triffa Plain ranges from 303 to 353 mm, with an overall average of approximately 326 mm and a low coefficient of variation (CV = 0.04), indicating relatively uniform interannual rainfall at the regional scale. Spatial interpolation highlights marked contrasts across the study area. Lower rainfall values are observed in the northwestern communes of Cap d’Eau, Madagh, and Boughriba, particularly around the CMV 105 and CMV 108 stations, where annual rainfall approaches 310–320 mm. In contrast, higher rainfall amounts are recorded near the Berkane commune, where annual rainfall reaches approximately 336 mm, influenced by the surrounding mountainous massifs. Cross-validation results show that Ordinary Kriging outperforms the IDW method, with a lower root mean square error (RMSE = 0.007 mm) and a mean error close to zero (0.000024 mm), compared to the IDW method (RMSE = 0.31 mm). These findings demonstrate that Ordinary Kriging provides more accurate and less biased rainfall estimates than deterministic methods in semi-arid Mediterranean environments characterized by sparse monitoring networks. The study identifies communes exposed to increasing water stress, particularly Cap d’Eau, Madagh, and Boughriba, and highlights the importance of geostatistical approaches for improving rainfall mapping and supporting climate-related water resource management in data-scarce regions.. Preprocessing Geostatistics Interpolation IDW Ordinary Kriging Statistics Cross Validation The Triffa plain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8765693","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592141461,"identity":"5aa01121-9297-4eda-815f-4d1c407af365","order_by":0,"name":"Mohammed 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Triffa plain","lastPublishedDoi":"10.21203/rs.3.rs-8765693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8765693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRainfall has shown significant variability during recent decades, particularly in semi-arid regions, where its spatial distribution strongly influences water availability and agricultural activities. In North Africa, rainfall remains irregular and unevenly distributed in space, making accurate spatial estimation essential for climate analysis and water resource management. The objective of this study is to assess rainfall spatial variability in the Triffa Plain (northeastern Morocco) and to identify the most reliable interpolation method under conditions of limited observational data. Annual rainfall data from nine stations covering the period 1992\u0026ndash;2021 (30 years) were first subjected to a pre-processing phase, including normality, homogeneity, and break detection tests. The results indicate that all rainfall series follow a normal distribution and show no significant breaks at confidence levels ranging from 90% to 99%, confirming the homogeneity and reliability of the dataset. Two commonly used spatial interpolation techniques were then applied and compared: the deterministic Inverse Distance Weighting (IDW) method and the geostatistical Ordinary Kriging approach. Cross-validation was used to evaluate the performance of both methods. The results reveal that mean annual rainfall over the Triffa Plain ranges from 303 to 353 mm, with an overall average of approximately 326 mm and a low coefficient of variation (CV\u0026thinsp;=\u0026thinsp;0.04), indicating relatively uniform interannual rainfall at the regional scale. Spatial interpolation highlights marked contrasts across the study area. Lower rainfall values are observed in the northwestern communes of Cap d\u0026rsquo;Eau, Madagh, and Boughriba, particularly around the CMV 105 and CMV 108 stations, where annual rainfall approaches 310\u0026ndash;320 mm. In contrast, higher rainfall amounts are recorded near the Berkane commune, where annual rainfall reaches approximately 336 mm, influenced by the surrounding mountainous massifs. Cross-validation results show that Ordinary Kriging outperforms the IDW method, with a lower root mean square error (RMSE\u0026thinsp;=\u0026thinsp;0.007 mm) and a mean error close to zero (0.000024 mm), compared to the IDW method (RMSE\u0026thinsp;=\u0026thinsp;0.31 mm). These findings demonstrate that Ordinary Kriging provides more accurate and less biased rainfall estimates than deterministic methods in semi-arid Mediterranean environments characterized by sparse monitoring networks. The study identifies communes exposed to increasing water stress, particularly Cap d\u0026rsquo;Eau, Madagh, and Boughriba, and highlights the importance of geostatistical approaches for improving rainfall mapping and supporting climate-related water resource management in data-scarce regions..\u003c/p\u003e","manuscriptTitle":"Geostatistical Analysis of Rainfall Spatial Variability and Interpolation Uncertainty in the Semi-Arid Triffa Plain (Northeast Morocco)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 08:45:25","doi":"10.21203/rs.3.rs-8765693/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"986573c0-d7a2-4781-b92e-ac0080214463","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T08:45:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 08:45:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8765693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8765693","identity":"rs-8765693","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}