It is essential to integrate marine and meteorological data to enhance effective adaptation to climate change, improve hazard forecasts, and inform maritime decision-making. This review examines the role of the International Hydrographic Organisation (IHO) S-100 standard in facilitating interaction between data interoperability and a wide range of marine and atmospheric datasets. A systematic literature review was conducted using the PRISMA guidelines, as 64 published articles from 2010 to 2024 were evaluated across databases, including Scopus, IEEE Xplore, and Web of Science. The research results indicate that semantic gaps are a barrier to integration, and interoperability in real-time is mentioned as a technical barrier to integration in 72% and 60% of studies, respectively. The use of S-100-based applications was most commonly reported in marine spatial planning, e-navigation systems, and digital charting systems. Despite the standard providing means to support syntactic integration, such as feature catalogues and registry services, it is not widely adopted across institutions and regions. The review identifies significant research gaps and recommends larger S-100 extensions in the meteorological spheres, as well as more advanced platforms for integration with artificial intelligence, including specific pilot projects in data-limited coastal areas. This paper contributes to the existing frameworks in the climate informatics literature. It treats S-100 not as a maritime data model, but as a foundational infrastructure for climate resilience and multi-sectoral interoperability.
References
[1]
Abdulameer, L., Al-Khafaji, M. S., Al-Awadi, A. T., Al Maimuri, N. M. L., Al-Shammari, M., Al-Dujaili, A. N. et al. (2025). Artificial Intelligence in Climate-Resilient Water Management: A Systematic Review of Applications, Challenges, and Future Directions. WaterConservationScienceandEngineering,10, Article No. 44. https://doi.org/10.1007/s41101-025-00371-2
[2]
Alexander, L., Brown, M., Greenslade, B., & Pharaoh, A. (2023). Development of IHO S-100—The New IHO Geospatial Standard for Hydrographic Data. TheInternationalHydrographicReview,29, 164-169. https://doi.org/10.58440/ihr-29-a18
[3]
Astle, H., & Schwarzberg, P. (2013). Towards a Universal Hydrographic Data Model. TransNav,theInternationalJournalonMarineNavigationandSafetyofSeaTransportation,7, 567-571. https://doi.org/10.12716/1001.07.04.12
[4]
Beale, T., Grain, H., & Hovenga, E. (2022). Standards for Digital Health, Known Limitations, and Procurement. In RoadmaptoSuccessfulDigitalHealthEcosystems (pp. 97-113). Elsevier. https://doi.org/10.1016/b978-0-12-823413-6.00019-7
[5]
Butkiewicz, T., Atkin, I., Sullivan, B., Kastrisios, C., Stevens, A., & Beregovyi, K. (2022). Web-Based Visualization of Integrated Next-Generation S-100 Hydrographic Datasets. In OCEANS 2022, Hampton Roads (pp. 1-7). IEEE. https://doi.org/10.1109/oceans47191.2022.9977133
[6]
Contarinis, S., Nakos, B., Tsoulos, L., & Palikaris, A. (2022). Web-Based Nautical Charts Automated Compilation from Open Hydrospatial Data. JournalofNavigation,75, 763-783. https://doi.org/10.1017/s0373463322000327
[7]
Contarinis, S., Pallikaris, A., & Nakos, B. (2020). The Value of Marine Spatial Open Data Infrastructures—Potentials of IHO S-100 Standard to Become the Universal Marine Data Model. JournalofMarineScienceandEngineering,8, Article No. 564. https://doi.org/10.3390/jmse8080564
[8]
Cooksey, C., & Datla, R. (2011). Workshop on Bridging Satellite Climate Data Gaps. JournalofResearchoftheNationalInstituteofStandardsandTechnology,116, 505-516. https://doi.org/10.6028/jres.116.002
[9]
Cui, H., & Zhu, S. (2024). Advancements in Marine Atmospheric Parameters Measurement: Integrated Dual-Channel LIDAR for Temperature, Humidity, and Wind Speed Estimation. JournalofPhysics:ConferenceSeries,2863, Article ID: 012009. https://doi.org/10.1088/1742-6596/2863/1/012009
[10]
Dalton, P. D., & Fornes, W. L. (2002). Building Support for an Integrated Ocean and Coastal Ocean Observing System. In Oceans ‘02 MTS/IEEE (pp. 1665-1670). IEEE. https://doi.org/10.1109/oceans.2002.1191884
[11]
del Rio, J., Toma, D. M., Martinez, E., O’Reilly, T. C., Delory, E., Pearlman, J. S. et al. (2018). A Sensor Web Architecture for Integrating Smart Oceanographic Sensors into the Semantic Sensor Web. IEEEJournalofOceanicEngineering,43, 830-842. https://doi.org/10.1109/joe.2017.2768178
[12]
Dhar, S., & Lindquist, P. (2012). Data Enhancement and Standardization Using AIS and GIS: A Public and Private Effort. JournalofMap&GeographyLibraries,8, 181-197. https://doi.org/10.1080/15420353.2012.698596
[13]
Dou, H.-L. (2011). Construction and Management of IHO S-100 Geospatial Information Registry. https://en.cnki.com.cn/Article_en/CJFDTOTAL-HYCH201103022.htm
[14]
Edmunds, M., Peddicord, D., & Frisse, M. E. (2015). Ten Reasons Why Interoperability Is Difficult. In C. A. Weaver et al. (Eds.), Healthcare Information Management Systems (pp. 127-137). Springer International Publishing. https://doi.org/10.1007/978-3-319-20765-0_7
[15]
Faulkner, A., Cornes, R., Chan, S., Siddons, J., & Kent, E. (2024). Pushing the Time and Space Resolution for Historical Marine Data: New Datasets of Sea-Surface Temperature and Marine Air Temperature. In EGU General Assembly 2024 (EGU24-16365). https://doi.org/10.5194/egusphere-egu24-16365
[16]
Freeman, E., Kent, E. C., Brohan, P., Cram, T., Gates, L., Huang, B. et al. (2019). The International Comprehensive Ocean-Atmosphere Data Set—Meeting Users Needs and Future Priorities. FrontiersinMarineScience,6, Article No. 435. https://doi.org/10.3389/fmars.2019.00435
[17]
Gong, N. (2018). Barriers to Adopting Interoperability Standards for Cyber Threat Intelligence Sharing: An Exploratory Study. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Computing (pp. 666-684). Springer International Publishing. https://doi.org/10.1007/978-3-030-01177-2_49
[18]
Jang, I., & Kim, M. (2015). Implementation of the Shore-Based Maritime Information Service Platform for E-Navigation Strategic Implementation Plan. JournalofNavigationandPortResearch,39, 157-163. https://doi.org/10.5394/kinpr.2015.39.3.157
[19]
Jiang, Y., Dou, J., Guo, Z., & Hu, K. (2015). Research of Marine Sensor Web Based on SOA and EDA. JournalofOceanUniversityofChina,14, 261-268. https://doi.org/10.1007/s11802-015-2492-5
[20]
Jirka, S., Autermann, C., Del Rio Fernandez, J., Konkol, M., & Martínez, E. (2023). Harmonising the Sharing of Marine Observation Data Considering Data Quality Information. In EGU General Assembly 2023 (EGU23-9291). https://doi.org/10.5194/egusphere-egu23-9291
[21]
Kanie, N., Suzuki, M., & Iguchi, M. (2013). Fragmentation of International Low-Carbon Technology Governance: An Assessment in Terms of Barriers to Technology Development. Global Environmental Research, 17, 61-70. http://www.airies.or.jp/attach.php/6a6f75726e616c5f31372d31656e67/save/0/0/17_1-8.pdf
[22]
Katikaneni, U., Ladner, R., & Petry, F. (2004). Internet Delivery of Meteorological and Oceanographic Data in Wide Area Naval Usage Environments. In Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters (p. 84). ACM Press. https://doi.org/10.1145/1013367.1013382
[23]
Khan, M. R., Peters, M., Sachweh, S., & Zundorf, A. (2014). AIS Based Communication Infrastructure and Data Aggregation for a Safer Seafaring. In 2014 2nd International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (pp. 35-41). IEEE. https://doi.org/10.1109/idaacs-sws.2014.6954620
[24]
Kim, M. S., Jang, I. S., & Lee, C. H. (2013). Design and Implementation of the Converged Platform for Geospaital and Maritime Information Service Based on S-100 Standard. JournalofKoreaSpatialInformationSociety,21, 23-32. https://doi.org/10.12672/ksis.2013.21.6.023
[25]
Klingsrisuk, R., Nitivattananon, V., Wongsurawat, W., & Sajor, E. (2013). Fragmentation in the Public Administration for Climate Change Mitigation: A Major Institutional Constraint for Energy Policy in the Transportation Sector of Thailand. EnvironmentAsia, 6, 1-10.
[26]
Kolukula, S. S., Baduru, B., Murty, P. L. N., Kumar, J. P., Rao, E. P. R., & Shenoi, S. S. C. (2020). Gaps Filling in HF Radar Sea Surface Current Data Using Complex Empirical Orthogonal Functions. PureandAppliedGeophysics,177, 5969-5992. https://doi.org/10.1007/s00024-020-02613-x
[27]
Kreuder-Sonnen, C., & Zürn, M. (2020). After Fragmentation: Norm Collisions, Interface Conflicts, and Conflict Management. GlobalConstitutionalism,9, 241-267. https://doi.org/10.1017/s2045381719000315
[28]
Lee, S., & Kim, H. (2023). IHO S-100 Data Model and Relevant Product Specification. TransNav,theInternationalJournalonMarineNavigationandSafetyofSeaTransportation,18, 297-301. https://doi.org/10.12716/1001.18.02.04
[29]
Lee, S., Lee, C., Kim, G., Na, H., Kim, H., Lee, J. et al. (2022). A Study of S-100 Based Product Specifications from a Software Implementation Point of View: Focusing on Data Model Representation, Similar Features and Symbols, and ECDIS and VTS Software. JournalofNavigation,75, 1226-1242. https://doi.org/10.1017/s037346332200039x
Liu, R. W., Liang, M., Nie, J., Deng, X., Xiong, Z., Kang, J. et al. (2021). Intelligent Data-Driven Vessel Trajectory Prediction in Marine Transportation Cyber-Physical System. In 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) (pp. 314-321). IEEE. https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00058
[32]
Luterbacher, J., Allan, R., Wilkinson, C., Hawkins, E., Teleti, P., Lorrey, A. et al. (2024). The Importance and Scientific Value of Long Weather and Climate Records; Examples of Historical Marine Data Efforts across the Globe. Climate,12, Article No. 39. https://doi.org/10.3390/cli12030039
[33]
Malone, T., Davidson, M., DiGiacomo, P., Gonçalves, E., Knap, T., Muelbert, J. et al. (2010). Climate Change, Sustainable Development and Coastal Ocean Information Needs. ProcediaEnvironmentalSciences,1, 324-341. https://doi.org/10.1016/j.proenv.2010.09.021
[34]
Mason, K., & Schröder-Fürstenberg, J. (2024). Exploring the Possibility of Adding DGGS Support to the S-100 Universal Hydrographic Data Model. TheInternationalHydrographicReview,30, 112-123. https://doi.org/10.58440/ihr-30-1-a01
[35]
Morlion, G. (2023). Development of S-401, Status of the New Standard for IECDIS. In Y. Li et al. (Eds.), Proceedings of PIANC Smart Rivers 2022 (pp. 834-837). Springer. https://doi.org/10.1007/978-981-19-6138-0_73
[36]
Murray O’Connor, H., & Cooper, J. A. G. (2024). Coastal and Marine Management—Navigating Islands of Data. MarinePolicy,169, Article ID: 106279. https://doi.org/10.1016/j.marpol.2024.106279
[37]
Nile, B. K., Al-Saadi, R. J. M., Abdulameer, L., Al Maimuri, N. M. L., & Al-Dujaili, A. N. (2025). Climate Change Impacts on River Hydraulics: A Global Synthesis of Hydrological Shifts, Ecological Consequences, and Adaptive Strategies. WaterConservationScienceandEngineering,10, Article No. 48. https://doi.org/10.1007/s41101-025-00375-y
[38]
Noone, S., Atkinson, C., Berry, D. I., Dunn, R. J., Freeman, E., Gonzalez, I. P., Kennedy, J. J., Kent, E. C., Kettle, A., Neill, S. M., Menne, M., Stephens, A., Thorne, P. W., Tucker, W., Voces, C., & Willet, K. M. (2021). Progress toward a Holistic Land and Marine Surface Meteorological Database and a Call for Additional Contributions. In EMS Annual Meeting 2021 (EMS2021-19). https://doi.org/10.5194/ems2021-19
[39]
Norden, M. F. van, Ladner, R. W., Arroyo-Suarez, E. N., & Office, N. O. (2008). Devel-oping a Concept of Operations for Military Surveys to IHO Standards without Shore-based Stations.
[40]
Oh, D., Park, D., & Park, S. (2016). Design of Maritime Meteorological Information Data Model Based on S-100. In K. J. Kim, & N. Joukov (Eds.), Information Science and Applications (ICISA) 2016 (pp. 851-859). Springer. https://doi.org/10.1007/978-981-10-0557-2_81
[41]
Palma, V., Giglio, D., & Tei, A. (2024). Investigating the Influence of E-Navigation and S-100 over the Computation of the Weather Route. WMUJournalofMaritimeAffairs,23, 457-475. https://doi.org/10.1007/s13437-024-00344-7
[42]
Park, D., & Park, S. (2014). Ontology Mapping for Enhanced Interoperability of S-100 Geographic Information Registers. InternationalJournalofSoftwareEngineeringandItsApplications,8, 225-234. https://doi.org/10.14257/ijseia.2014.8.1.20
[43]
Park, D., & Park, S. (2015). E-Navigation-Supporting Data Management System for Variant S-100-Based Data. MultimediaToolsandApplications,74, 6573-6588. https://doi.org/10.1007/s11042-014-2242-5
[44]
Park, D., & Park, S. (2017). Syntactic-Level Integration and Display of Multiple Domains’ S-100-Based Data for E-navigation. ClusterComputing,20, 721-730. https://doi.org/10.1007/s10586-017-0754-2
[45]
Park, D., Kwon, H., & Park, S. (2013). Design and Implementation of Feature Catalogue Builder Based on the S-100 Standard. KIPSTransactionsonSoftwareandDataEngineering,2, 571-578. https://doi.org/10.3745/ktsde.2013.2.8.571
[46]
Park, G., Park, D., & Park, S. (2014). Design and Implementation of Display Module for Electronic Navigational Chart Data. In 2014 International Conference on IT Convergence and Security (ICITCS) (pp. 1-3). IEEE. https://doi.org/10.1109/icitcs.2014.7021757
[47]
Partescano, E., Brosich, A., Lipizer, M., Cardin, V., & Giorgetti, A. (2017). From Heterogeneous Marine Sensors to Sensor Web: (Near) Real-Time Open Data Access Adopting OGC Sensor Web Enablement Standards. OpenGeospatialData,SoftwareandStandards,2, Article No. 22. https://doi.org/10.1186/s40965-017-0035-2
[48]
Pearlman, J., Schaap, D., & Glaves, H. (2016). Ocean Data Interoperability Platform (ODIP): Addressing Key Challenges for Marine Data Management on a Global Scale. In OCEANS 2016 MTS/IEEE Monterey (pp. 1-7). IEEE. https://doi.org/10.1109/oceans.2016.7761406
[49]
Percivall, G. (2010). The Application of Open Standards to Enhance the Interoperability of Geoscience Information. InternationalJournalofDigitalEarth,3, 14-30. https://doi.org/10.1080/17538941003792751
[50]
Rack, F., Milne, P., Fippinger, P., & Jahnke, R. (2005). Emerging Needs and Existing Links in Distributed Ocean Science Research Data Sets. In 2005 IEEE International Symposium on Mass Storage Systems and Technology (pp. 9-13). IEEE. https://doi.org/10.1109/lgdi.2005.1612457
[51]
Ramar, K., Appovoo, R., Shanmugasundaram, H. et al. (2022). Semantic Heterogeneity Management between Weather Systems Using Ontology Mapping. In 2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) (pp. 52-57). IEEE. https://doi.org/10.1109/centcon56610.2022.10051484
[52]
Reigosa, C. J., Serrano, M., Justavino, M., & Villarreal, V. (2024). Integrated Climate Observation and Analysis System for Scientific Research. In 2024 IEEE VII Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC) (pp. 1-5). IEEE. https://doi.org/10.1109/amitic62658.2024.10747588
[53]
Rødseth, Ø. J. (2016). Integrating IEC and ISO Information Models into the S-100 Common Maritime Data Structure. Norwegian Marine Technology Research Institute. https://sintef.brage.unit.no/sintef-xmlui/bitstream/handle/11250/2382247/IECandISO_S-100.pdf?sequence=3&isAllowed=y
[54]
Schaap, D. M. A., & Lowry, R. K. (2010). SeaDataNet—Pan-European Infrastructure for Marine and Ocean Data Management: Unified Access to Distributed Data Sets. InternationalJournalofDigitalEarth,3, 50-69. https://doi.org/10.1080/17538941003660974
[55]
Snowden, D., Tsontos, V. M., Handegard, N. O., Zarate, M., O’ Brien, K., Casey, K. S. et al. (2019). Data Interoperability between Elements of the Global Ocean Observing System. FrontiersinMarineScience,6, Article No. 442. https://doi.org/10.3389/fmars.2019.00442
[56]
Thorne, P. W., Allan, R. J., Ashcroft, L., Brohan, P., Dunn, R. J. H., Menne, M. J. et al. (2017). Toward an Integrated Set of Surface Meteorological Observations for Climate Science and Applications. BulletinoftheAmericanMeteorologicalSociety,98, 2689-2702. https://doi.org/10.1175/bams-d-16-0165.1
[57]
Tsontos, V., Quach, N., Thompson, C., Platt, F., Roberts, J., Lam, C. H., & Arms, S. C. (2017). The Oceanographic in Situ Data Interoperability Project (OIIP)—A Year in Review. In OCEANS 2017 (pp. 1-7). IEEE. https://ieeexplore.ieee.org/document/8232345
[58]
Vollmer, M., Berchtold, C., Sakkas, G., Tsaloukidis, I., Kazantzidou-Firtinidou, D., Woitsch, P. et al. (2024). Standardization Gaps in European Disaster Management. JournalofHomelandSecurityandEmergencyManagement,21, 209-242. https://doi.org/10.1515/jhsem-2021-0047
[59]
Walker, D. M., Tarver, W. L., Jonnalagadda, P., Ranbom, L., Ford, E. W., & Rahurkar, S. (2023). Perspectives on Challenges and Opportunities for Interoperability: Findings from Key Informant Interviews with Stakeholders in Ohio. JMIR Medical Informatics, 11, e43848. https://doi.org/10.2196/preprints.43848
[60]
Ward, R., Alexander, L., & Greenslade, B. (2009). IHO S-100: The New Hydrographic Geospatial Standard for Marine Data and Information. The International Hydrographic Review, 1.
[61]
Watson, S. (2004). Researchers Converge to Improve Metadata Standards. Eos,TransactionsAmericanGeophysicalUnion,85, 91. https://doi.org/10.1029/2004eo090004
[62]
Xie, L., & Liu, B. (2014). Weather Forecasting|Marine Meteorology. In EncyclopediaofAtmosphericSciences (pp. 287-292). Elsevier. https://doi.org/10.1016/b978-0-12-382225-3.00212-7
[63]
Xie, L., & Liu, B. (2024). Weather Forecasting|Marine Meteorology. In ReferenceModuleinEarthSystemsandEnvironmentalSciences. Elsevier. https://doi.org/10.1016/b978-0-323-96026-7.00025-4
[64]
Yousefi, K. P., & Kollet, S. (2022). Closing the Gap between Models and Observations: Deep Learning from Mismatches&160. In The EGU General Assembly 2022. https://doi.org/10.5194/egusphere-egu22-13008