Assessment of Hydrological Loading Displacement from GNSS and GRACE Data Using Deep Learning Algorithms

Assessment of Hydrological Loading Displacement from GNSS and GRACE Data Using Deep Learning Algorithms | 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 Article Assessment of Hydrological Loading Displacement from GNSS and GRACE Data Using Deep Learning Algorithms Changshou Wei, Maosheng Zhou, Zhixing Du, Lijing Han, Hao Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4917007/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This work introduces a novel method for inverting hydrological loading displacement using 3D Convolutional Neural Networks (3D-CNN). This approach utilizes vertical displacement time series data from 41 Global Navigation Satellite System (GNSS) stations across Yunnan Province, China, and its adjacent areas, coupled with spatiotemporal variations in terrestrial water storage derived from the GRACE satellite. The 3D-CNN method demonstrates markedly higher inversion accuracy compared to conventional load Green's function inversion techniques. This improvement is evidenced by substantial reductions in deviations from GNSS observations across various statistical metrics: the maximum deviation decreased by 1.34 millimeters, the absolute minimum deviation by 1.47 millimeters, the absolute mean deviation by 79.6%, and the standard deviation by 31.4%. An in-depth analysis of terrestrial water storage and loading displacement from 2019 to 2022 in Yunnan Province revealed distinct seasonal fluctuations and a rising trend, primarily driven by dominant annual and semi-annual cycles. These cycles accounted for over 90% of the variance, with an annual increase of 1.83 millimeters. The spatial distribution of water load displacement is strongly associated with regional precipitation patterns, showing smaller amplitudes in the northeast and northwest and larger amplitudes in the southwest. This pattern underscores the significant impact of precipitation on changes in terrestrial water storage. This research findings underscore the efficacy of deep learning techniques in inverting Earth geophysical parameters and offer fresh perspectives on regional water cycle dynamics. This has profound implications for water resource management and adapting to climate change. Earth and environmental sciences/Hydrology Earth and environmental sciences/Solid earth sciences 3D-CNN hydrological loading displacement GRACE GNSS load Green's function Full Text Additional Declarations No competing interests reported. Supplementary Files 12yunnangridhydmodel.csv 3DCNN12yunnan.csv Datadeclaration20240827.docx GFyunnnan.xlsx yunnangnssdata.csv yunnanmasconfiltered.csv Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviews received at journal 13 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviewers invited by journal 04 Sep, 2024 Editor assigned by journal 04 Sep, 2024 Editor invited by journal 29 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 15 Aug, 2024 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-4917007","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":353852424,"identity":"88dc31d9-0018-4ae4-accf-da13c18483fe","order_by":0,"name":"Changshou Wei","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Changshou","middleName":"","lastName":"Wei","suffix":""},{"id":353852425,"identity":"595c983d-11bd-48f6-88e9-42e296e655b7","order_by":1,"name":"Maosheng 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