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Future sea-level rise will likely expand the inland extent of storm surge inundation and, in turn, increase the vulnerability of the people, properties and economies of coastal communities. Modeling future storm surge inundation enhanced by sea-level rise uses numerous data sources with inherent uncertainties. There is uncertainty in (1) hydrodynamic storm surge models, (2) future sea-level rise projections, and (3) topographic digital elevation models representing the height of the coastal land surface. This study implemented a Monte Carlo approach to incorporate the uncertainties of these data sources and model the future 1% flood zone extent in the Tottenville neighborhood of New York City (NYC) in a probabilistic, geographical information science (GIS) framework. Generated spatiotemporal statistical products indicate a range of possible future flood zone extents that results from the uncertainties of the data sources and from the terrain itself. Small changes in the modeled land and water heights within the estimated uncertainties of the data sources results in larger uncertainty in the future flood zone extent in low-lying areas with smaller terrain slope. An interactive web map, UncertainSeas. com, visualizes these statistical products and can inform coastal management policies to reduce the vulnerability of Tottenville, NYC to future coastal inundation.
The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program.
<p>Magnetic maps depict spatial variations in the Earth&#8217;s magnetic field.&#160; These variations occur at a wide range of scales and are produced via a variety of physical processes related to factors including structure and evolution of the Earth&#8217;s core field and the geologic distribution of magnetic minerals in the lithosphere.&#160; Mankind has produced magnetic maps for 100&#8217;s of years with increasing fidelity and accuracy and there is a general understanding (particularly among the geophysicists who produce and use these maps) of the approximate level of resolution and accuracy of these maps.&#160; However, few magnetic maps, or the digital grids that typically underpin these maps, have been produced with accompanying uncertainty quantification.&#160; When uncertainty is addressed, it is typically a statistical representation at the grid or survey level (e.g., +- 10 nT overall uncertainty based on line crossings for a modern airborne survey) and not at the cell by cell local level.</p><p>As magnetic map data are increasingly used in complex inversions and in combination with other data or constraints (including in machine learning applications), it is increasingly important to have a handle on the uncertainties in these data.&#160; An example of an application with need for detailed uncertainty estimation is the use of magnetic map information for alternative navigation.&#160; In this application data from an onboard magnetometer is compared with previously mapped (or modeled) magnetic variations.&#160; The uncertainty of this previously mapped information has immediate implications for the potential accuracy of navigation.</p><p>We are exploring the factors contributing to magnetic map uncertainty and producing uncertainty estimates for testing using new data collection in previously mapped (or modeled) map areas.&#160; These factors include (but are likely not limited to) vintage and type of measured data, spatial distribution of measured data, expectation of magnetic variability (e.g., geologic or geochemical environment), statistics of redundant measurement, and spatial scale/resolution of the magnetic map or model.&#160; The purpose of this talk is to discuss the overall issue and our initial results and solicit feedback and ideas from the interpretation community.</p>
The rate of future global sea-level rise will likely increase due to elevated ocean temperatures and increases in land-ice melt. Nearly 40 percent of the U.S. population lives in coastal communities, and coastal properties are expected to become more prone to coastal flooding in the coming decades due to relative sea-level rise caused by both global and local factors. Understanding how this projected sea-level rise translates to lost economic value is critical to the decisions of insurance companies, banks, governments, investors, and regulatory agencies. We use probability distributions of local sea-level rise projections, National Oceanic and Atmospheric (NOAA) coastal digital elevation models, and CoreLogic housing data to estimate a range of housing market value impairments from future sea-level rise in 15 major U.S. coastal cities as well as the associated timing of those impairments. Our estimates include only residential properties with four or fewer units and thus provide a lower bound estimate of economic risk from sea-level rise. We estimate that within these 15 major U.S. coastal metros, sea-level rise will inundate between 2,000 and 28,000 properties by 2100 in a relatively low greenhouse gas concentration scenario and between 7,000 to 77,000 properties under an unlikely, extreme greenhouse gas concentration scenario.
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