GNSS Evaluation of GRACE-Assimilated Water Storage Models Over 89 River Basins Worldwide

GNSS Evaluation of GRACE-Assimilated Water Storage Models Over 89 River Basins Worldwide | 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 GNSS Evaluation of GRACE-Assimilated Water Storage Models Over 89 River Basins Worldwide Majid Abbaszadeh, Tonie van Dam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6611365/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 The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GFO) gravity observations have significantly improved our understanding of the terrestrial water cycle. However, GRACE-assimilated (GA) hydrological models still differ significantly. This paper uses Global Navigation Satellite System (GNSS) data to assess two global GA datasets: Global Land Water Storage Release 2 (GLWS2.0) and Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). From 2004 to 2019, the mean annual amplitude of equivalent water thickness (EWT) of these datasets differs by more than 25 mm over 40% of the modeled land area, and the timing of peak water storage diverges by as much as 30-days across 50% of their domain. We compare the modeled hydrological loading vertical displacement predicted from these models with GNSS uplift data to compare and contrast the model quality. Using river basin boundary information from 89 rivers, we cluster 9,173 global GNSS stations, each with at least three years of daily data. Results show that CLSM-DA generally agrees better with GNSS data across more river basins. Its 100–300 mm larger annual water variation accounts for better agreement in Africa, Southeast Asia, and parts of South America. In regions like the Western United States and Eastern Europe, where both models estimate similar annual amplitudes, CLSM-DA’s 30–60 day phase delay improves alignment with GNSS. This evaluation also reveals key limitations in both models, especially during extreme hydrological events such as droughts, and highlights the value of geodetic observations in advancing GA hydrological modeling. Hydrological loading GNSS GRACE Data assimilation 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. 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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-6611365","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491423815,"identity":"e0264afd-4158-42c2-8591-87646a2a6f58","order_by":0,"name":"Majid Abbaszadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHCChAM8QMIAzGZjSOAHUgcYCkjRItkA0mJAwB4ULQYHQAw8WnTbDzw88IbBLs+c/fjjDz/KbPKMb+QeBNpyGKcWszMJCQfnMCQXW/bkGBj2nEsrNruRl4Bfy4GEhMM8DMyJGw7kMCTwth1O3HYjxwC/lvMPQFrqEzecf/7g4N+2/4mbZxDScgNsy+HEDTcSDJt52w4kbpAgqOUB0C8Gx4stZ7wxZpY5l5w448wbgwMJBul4HJaT/OFNRXWeOX/6449vyuwS+9tzjD98qLDGqQUYKQlYYiEBjwYGBvYDeKVHwSgYBaNgFDAAADukYuUpeTr/AAAAAElFTkSuQmCC","orcid":"","institution":"University of Luxembourg","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Abbaszadeh","suffix":""},{"id":491423816,"identity":"3af053e1-41ec-403a-ba82-80793459faf6","order_by":1,"name":"Tonie van Dam","email":"","orcid":"","institution":"University of Utah","correspondingAuthor":false,"prefix":"","firstName":"Tonie","middleName":"van","lastName":"Dam","suffix":""}],"badges":[],"createdAt":"2025-05-07 11:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6611365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6611365/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92748832,"identity":"2017f9eb-efa4-480d-90df-efcef4a27b49","added_by":"auto","created_at":"2025-10-03 20:31:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2166307,"visible":true,"origin":"","legend":"","description":"","filename":"AbbaszadehvanDam.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6611365/v1_covered_624907ec-0057-4e8f-8fc7-8d26dd1b0f37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GNSS Evaluation of GRACE-Assimilated Water Storage Models Over 89 River Basins Worldwide","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Hydrological loading, GNSS, GRACE, Data assimilation","lastPublishedDoi":"10.21203/rs.3.rs-6611365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6611365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GFO) gravity observations have significantly improved our understanding of the terrestrial water cycle. However, GRACE-assimilated (GA) hydrological models still differ significantly. This paper uses Global Navigation Satellite System (GNSS) data to assess two global GA datasets: Global Land Water Storage Release 2 (GLWS2.0) and Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA).\u003c/p\u003e\u003cp\u003eFrom 2004 to 2019, the mean annual amplitude of equivalent water thickness (EWT) of these datasets differs by more than 25 mm over 40% of the modeled land area, and the timing of peak water storage diverges by as much as 30-days across 50% of their domain. We compare the modeled hydrological loading vertical displacement predicted from these models with GNSS uplift data to compare and contrast the model quality. Using river basin boundary information from 89 rivers, we cluster 9,173 global GNSS stations, each with at least three years of daily data.\u003c/p\u003e\u003cp\u003eResults show that CLSM-DA generally agrees better with GNSS data across more river basins. Its 100\u0026ndash;300 mm larger annual water variation accounts for better agreement in Africa, Southeast Asia, and parts of South America. In regions like the Western United States and Eastern Europe, where both models estimate similar annual amplitudes, CLSM-DA\u0026rsquo;s 30\u0026ndash;60 day phase delay improves alignment with GNSS. This evaluation also reveals key limitations in both models, especially during extreme hydrological events such as droughts, and highlights the value of geodetic observations in advancing GA hydrological modeling.\u003c/p\u003e","manuscriptTitle":"GNSS Evaluation of GRACE-Assimilated Water Storage Models Over 89 River Basins Worldwide","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 09:11:50","doi":"10.21203/rs.3.rs-6611365/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":"8512fb50-bc24-46d8-af81-68aac1ead5c4","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-11T07:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 09:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6611365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6611365","identity":"rs-6611365","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}