{
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"name": "Hana R Thurman",
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{
"name": "Nicholas M Enwright",
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{
"name": "Michael J Osland",
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},
{
"name": "Davina L Passeri",
"nameType": "Personal",
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"United States Geological Survey"
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"nameIdentifier": "https://orcid.org/0000-0002-9760-3195",
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},
{
"name": "Richard H Day",
"nameType": "Personal",
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"United States Geological Survey"
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{
"name": "Bethanie M Simons",
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"titles": [
{
"title": "Sea-level rise and high tide flooding inundation probability and depth statistics at De Soto National Memorial, Florida"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2024,
"subjects": [
{
"subject": "ecology"
},
{
"subject": "remote sensing"
},
{
"subject": "geography"
}
],
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"rights": "Creative Commons Zero v1.0 Universal",
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"descriptions": [
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"description": "This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service\u2019s De Soto National Memorial. These datasets were developed using 1-m digital elevation model (DEM) from the 3D Elevation program. This data release includes results from analyses of two local sea-level rise scenarios for two-time steps \u2014 the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by the National oceanic and Atmospheric Administration (NOAA). We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability \u22640.33; 2) Likely as not, probability >0.33 and \u22640.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.",
"descriptionType": "Abstract"
}
],
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"created": "2024-01-05T14:23:17Z",
"registered": "2024-01-05T14:23:17Z",
"published": null,
"updated": "2024-06-23T08:16:08Z"
}
}