Integrated groundwater vulnerability assessment in the Pench River Basin, Madhya Pradesh, Central India using DRASTIC-based models

Integrated groundwater vulnerability assessment in the Pench River Basin, Madhya Pradesh, Central India using DRASTIC-based models | 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 Integrated groundwater vulnerability assessment in the Pench River Basin, Madhya Pradesh, Central India using DRASTIC-based models Chandra Shekhar Dwivedi, Amarjeet Kumar Mahato, Arvind Chandra Pandey, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7074657/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 21 You are reading this latest preprint version Abstract Groundwater vulnerability assessment is vital for sustainable management, especially in regions with intensive agricultural and urban activities. This study evaluated groundwater susceptibility in the Pench River Basin using DRASTIC, DRASTIC-AHP, and modified DRASTIC-AHP models. Seven key hydrogeological parameters, net recharge, aquifer media, depth to water, soil media, slope, hydraulic conductivity, and vadose zone impact, were integrated, along with Land Use and Land Cover (LULC) in the modified model. Vulnerability indices ranged from 43 to 182, categorizing the area into five classes: very low (17.78%), low (21.67%), moderate (24.24%), high (19.31%), and very high (17.00%). Central and southern regions exhibited heightened vulnerability due to shallow water tables (< 3.56 mbgl) and high recharge rates (38% of the area). Urban (38%) and agricultural zones (29%) were identified as high-risk areas, while vegetation (23%) and barren land (10%) showed reduced vulnerability. Validation with 84 groundwater samples revealed fluoride concentrations (> 0.63 mg/L) strongly correlated (𝑅²=0.45) with vulnerability, demonstrating the modified DRASTIC-AHP model's superior accuracy and applicability. Findings indicate excessive fertilizer use and improper sewage disposal as major contributors to contamination, necessitating targeted interventions like regulating agricultural runoff, managing urban wastewater, and implementing artificial recharge. This study underscores the importance of integrating LULC into vulnerability assessments and highlights the need for advanced modeling to improve precision and support sustainable groundwater management. Groundwater Vulnerability GIS-based Assessment DRASTIC Model Aquifer Contamination Analytic Hierarchy Process (AHP) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviews received at journal 06 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 12 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 08 Jul, 2025 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. 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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-7074657","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511890874,"identity":"7ecf3118-cc8a-4fc2-b2bf-f576aa06c388","order_by":0,"name":"Chandra Shekhar Dwivedi","email":"","orcid":"","institution":"Central University of Jharkhand","correspondingAuthor":false,"prefix":"","firstName":"Chandra","middleName":"Shekhar","lastName":"Dwivedi","suffix":""},{"id":511890875,"identity":"8d7e0d7e-9d76-4154-953a-921e8756b133","order_by":1,"name":"Amarjeet Kumar Mahato","email":"","orcid":"","institution":"Central University of Jharkhand","correspondingAuthor":false,"prefix":"","firstName":"Amarjeet","middleName":"Kumar","lastName":"Mahato","suffix":""},{"id":511890876,"identity":"feb26f0f-52f8-47de-ab00-2d7fc2ec75bd","order_by":2,"name":"Arvind Chandra Pandey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYJCCAwxsYNqAgaGCgYGxAcomUssZIrUwwLUwthHhKP7ZZwwPF5Qx5PHPbt74mHeeXWJzA/PDDwwFd3BqkTiXY3B4xjmGYok7x4qNebclJzY2sBlLMBg8w23NGbaEw7xtDIkNN3LMpHm3HQBqYTADOvIwTh3yMC3zb+SY/+adA9LC/g2vFoMzzAfAWjYAbWHmbQBp4cFviyFIC885iWLDG2nFknOOJRs3NvMUSyTg0SJ3hrH5M0+ZTZ7cjeSNH97U2MlubG/f+OHDH9xaoEAiAUQy8YDsbQYSCYQ0wNQw/gAS8kSoHgWjYBSMgpEFABCcVUie1nIXAAAAAElFTkSuQmCC","orcid":"","institution":"Central University of Jharkhand","correspondingAuthor":true,"prefix":"","firstName":"Arvind","middleName":"Chandra","lastName":"Pandey","suffix":""},{"id":511890877,"identity":"3ad7ea8d-f1ac-4861-b11f-81bd67d9063a","order_by":3,"name":"Sagar Kumar Swain","email":"","orcid":"","institution":"Central University of Jharkhand","correspondingAuthor":false,"prefix":"","firstName":"Sagar","middleName":"Kumar","lastName":"Swain","suffix":""},{"id":511890878,"identity":"e6dd746c-93f2-42b2-995e-50200d51a50f","order_by":4,"name":"Bikash Ranjan Parida","email":"","orcid":"","institution":"Central University of Jharkhand","correspondingAuthor":false,"prefix":"","firstName":"Bikash","middleName":"Ranjan","lastName":"Parida","suffix":""}],"badges":[],"createdAt":"2025-07-08 12:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7074657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7074657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90943943,"identity":"52b18049-8221-48d6-808a-4e785821f4d9","added_by":"auto","created_at":"2025-09-09 19:26:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1590911,"visible":true,"origin":"","legend":"","description":"","filename":"DRASTICmodifiedmanuscript11.07.2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7074657/v1_covered_02f0e391-294e-4038-8c51-634107f6c931.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated groundwater vulnerability assessment in the Pench River Basin, Madhya Pradesh, Central India using DRASTIC-based models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Groundwater Vulnerability, GIS-based Assessment, DRASTIC Model, Aquifer Contamination, Analytic Hierarchy Process (AHP)","lastPublishedDoi":"10.21203/rs.3.rs-7074657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7074657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater vulnerability assessment is vital for sustainable management, especially in regions with intensive agricultural and urban activities. This study evaluated groundwater susceptibility in the Pench River Basin using DRASTIC, DRASTIC-AHP, and modified DRASTIC-AHP models. Seven key hydrogeological parameters, net recharge, aquifer media, depth to water, soil media, slope, hydraulic conductivity, and vadose zone impact, were integrated, along with Land Use and Land Cover (LULC) in the modified model. Vulnerability indices ranged from 43 to 182, categorizing the area into five classes: very low (17.78%), low (21.67%), moderate (24.24%), high (19.31%), and very high (17.00%). Central and southern regions exhibited heightened vulnerability due to shallow water tables (\u0026lt;\u0026thinsp;3.56 mbgl) and high recharge rates (38% of the area). Urban (38%) and agricultural zones (29%) were identified as high-risk areas, while vegetation (23%) and barren land (10%) showed reduced vulnerability. 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