Item talk:Q318748

{

 "id": "10.5066/p90gfuk5",
 "attributes": {
   "doi": "10.5066/p90gfuk5",
   "identifiers": [],
   "creators": [
     {
       "name": "Dean B Gesch",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-8992-4933",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Jeffrey J Danielson",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0003-0907-034X",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Juddson Nelson",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Ryan L. Perroy",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-4210-3281",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Eszter Collier",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Heather A Kerkering",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "Inundation Exposure Assessment for Select Islands in the Republic of the Marshall Islands"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2024,
   "subjects": [
     {
       "subject": "remote sensing"
     },
     {
       "subject": "geography"
     }
   ],
   "contributors": [],
   "dates": [],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
   "relatedIdentifiers": [],
   "relatedItems": [],
   "sizes": [],
   "formats": [],
   "version": null,
   "rightsList": [],
   "descriptions": [
     {
       "description": "As a low-lying island nation, the Republic of the Marshall Islands (RMI) is at the forefront of exposure to climate change impacts, including, primarily, inundation (coastal flooding). Increased water levels can stem from episodic events (storm surge, wave run-up, king tides) or from chronic conditions (long term sea-level rise). Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, accurate coastal elevation data are a critical input for assessments of inundation exposure and vulnerability. Previous research has demonstrated that the quality of data used for elevation-based assessments must be well understood and applied to properly model potential impacts. The vertical uncertainty of the input elevation data controls to a large extent the increments of water level increase and planning horizons that can be effectively used in an assessment. Recent high-resolution elevation data along the coast, such as the digital elevation models (DEMs) used here, exhibit high vertical accuracy, and thus have become indispensable for inundation exposure assessments. When properly characterized, the vertical accuracy of the high-resolution, high-accuracy elevation data can be used to generate maps and report assessment results with the uncertainty stated in terms of a specific confidence level, which is the approach employed here. This data release includes the results of a quantitative assessment of inundation exposure for five selected sites in RMI (Aur Island and Tobal Island in Aur Atoll, Ebon Island in Ebon Atoll, Likiep Island in Likiep Atoll, and Mejit Island), including rigorous accounting for the vertical uncertainty in the input elevation model data. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying land (no direct hydrologic flowpath to the ocean) were mapped and characterized for different inundation levels.",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/657b977bd34e23d35331b138",
   "contentUrl": null,
   "metadataVersion": 1,
   "schemaVersion": "http://datacite.org/schema/kernel-4",
   "source": "api",
   "isActive": true,
   "state": "findable",
   "reason": null,
   "viewCount": 0,
   "downloadCount": 0,
   "referenceCount": 0,
   "citationCount": 0,
   "partCount": 0,
   "partOfCount": 0,
   "versionCount": 0,
   "versionOfCount": 0,
   "created": "2024-06-07T18:14:36Z",
   "registered": "2024-06-07T18:14:36Z",
   "published": null,
   "updated": "2024-06-07T18:15:18Z"
 },
 "relationships": {
   "client": {
     "data": {
       "id": "usgs.prod",
       "type": "clients"
     }
   }
 },
 "type": "dois"

}