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Their future evolution poses a significant challenge, as it is estimated that **80% of the global population will live within 40 kilometers of the coast by 2030**.\n\nAt the intersection of land and sea, **estuaries, lagoons, and deltas** experience daily interactions between freshwater and marine waters. The major estuaries of France (*Seine, Loire, and Gironde*) and the Rhône delta are just a few examples of these \"transitional\" environments along the French coastline.\n\nCurrently, the **885 coastal municipalities of mainland France** account for over **10% of the population**. According to recent estimates (*source: Ministry of Ecological Transition*), an additional **4.5 million inhabitants** are expected to settle in these regions by 2040. In such conditions, the **coastal ocean environment** in these areas will become increasingly vulnerable to various combined human and climate-induced stressors:\n\n- Warming \n- Acidification \n- Eutrophication* \n- Hypoxia* \n- Contamination \n- Extreme events \n\nSimulating their evolution under the combined influence of **anthropogenic pressure and climate change** is a major challenge. The number of unknown and often unpredictable parameters is considerable. Addressing this requires a **comprehensive approach**, grounded in the **multidisciplinary analysis** of the numerous environmental factors affecting these coastal zones. \n\nThe knowledge generated will enable the **co-construction of scientifically based solutions** with environmental managers for the **sustainable development** of coastal areas.\n\n---\n\n## An Original, Multidisciplinary, and Innovative Integrated Approach\n\nSpanning **six years (2022-2028)**, the RIOMar (*River-dominated Ocean Margins*) project aims to tackle all these challenges. Scientific studies within the project focus on five major coastal areas in mainland France influenced by large rivers:\n\n- The Seine \n- The Loire \n- The Gironde \n- The Rhône \n- The Charente \n\nVarious scenarios for future evolution will be explored in collaboration with numerous stakeholders from these regions. **Relevant indicators** will be developed, in consultation with environmental managers, to propose **tailored and sustainable solutions** for public policies.\n\n---\n\n### Glossary\n\n- **Eutrophication**: The excessive enrichment of water with nutrients, leading to algal blooms and oxygen depletion. \n- **Hypoxia**: Low oxygen levels in water, threatening aquatic life." } ], "http://schema.org/license" : [ { "@id" : "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name" : [ { "@value" : "RIOMAR project (2024-2030)" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2025-01-25 15:34:20.283153+00:00" } ] }, { "@id" : "https://data-fair2adapt.ifremer.fr/riomar//misc/croco_grd.nc", "@type" : [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://data-fair2adapt.ifremer.fr/riomar//misc/croco_grd.nc" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2025-01-25 16:54:25.280358+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2025-10-14 08:33:37.297165+00:00" } ], "http://schema.org/description" : [ { "@value" : "This is the grid of the Croco model used to generate RiOMar data." } ], "http://schema.org/encodingFormat" : [ { "@value" : "application/x-netcdf" } ], "http://schema.org/license" : [ { "@id" : "https://creativecommons.org/licenses/by-nc/4.0/legalcode" } ], "http://schema.org/name" : [ { "@value" : "Croco grid netCDF file" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2025-01-25 16:54:25.280358+00:00" } ] }, { "@id" : "https://data-fair2adapt.ifremer.fr/riomar/GAMAR/", "@type" : [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://data-fair2adapt.ifremer.fr/riomar/GAMAR/" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2025-01-25 15:43:13.218913+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2025-10-14 08:33:31.079615+00:00" } ], "http://schema.org/description" : [ { "@value" : "All the RiOMar datasets were generated on the HPC of IFREMER (Brest, France) and made accessible to everyone on the IFREMER DATAMOR." } ], "http://schema.org/license" : [ { "@id" : "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name" : [ { "@value" : "RiOMar datasets" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2025-01-25 15:43:13.218913+00:00" } ] }, { "@id" : "https://data-fair2adapt.ifremer.fr/riomar/GAMAR/GAMAR_1h_inst_Y2006M01.nc", "@type" : [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://data-fair2adapt.ifremer.fr/riomar/GAMAR/GAMAR_1h_inst_Y2006M01.nc" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2025-01-25 16:56:08.727634+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2025-10-14 08:33:36.972118+00:00" } ], "http://schema.org/description" : [ { "@value" : "Raw RiOMar dataset generated by the Croco RiOMar numerical model. 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], "http://schema.org/dateModified" : [ { "@value" : "2025-10-14 08:33:32.309746+00:00" } ], "http://schema.org/description" : [ { "@value" : "A sample dataset in DGGS Level 14 Healpix" } ], "http://schema.org/license" : [ { "@id" : "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name" : [ { "@value" : "RiOMar DGGS Healpix Level 14 data" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2025-01-25 15:51:30.560593+00:00" } ] }, { "@id" : "https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#ConferencePaper" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2025-01-25 15:39:18.552821+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2025-10-14 08:33:30.776384+00:00" } ], "http://schema.org/description" : [ { "@value" : "## Abstract \nTraditional map projections introduce distortions, especially for global data. Discrete Global Grid Systems (DGGS) offer an alternative by dividing the Earth into equal-area grid cells at different resolutions. This paper describes xdggs, a new Xarray extension that simplifies working with DGGS. Xdggs provides a unified API for various DGGS libraries and integrates seamlessly with the Pangeo ecosystem through extending the widely used Xarray library to use the DGGS-specific cell identifiers as an index. This development makes DGGS more accessible and will lead to facilitating data analysis on a planetary scale.\nXdggs aims to provide a user-friendly API that hides the implementation complexities of different DGGS libraries. And because it integrates seamlessly with Xarray, a popular tool for geospatial data analysis, xdggs promotes FAIR data practices by simplifying data access and interoperability and can become a valuable tool for geospatial scientists and application developers working with global datasets.\n\n## How to cite\n\nKmoch, A., Bovy, B., Magin, J., Abernathey, R., Coca-Castro, A., Strobl, P., Fouilloux, A., Loos, D., Uuemaa, E., Chan, W. T., Delouis, J.-M., and Odaka, T.: XDGGS: A community-developed Xarray package to support planetary DGGS data cube computations, Int. Arch. Photogramm. Remote Sens. Spatial Inf. 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