 to create the current sea level models. The HOWL represents the highest recorded water level at that station and the date on which that observation was made. Therefore the HOWL data is completely dependent upon the length of time that the tidal station has been in existence. The oldest HOWL was recorded in 1898.
This data was used to model the base year (2000) surface representing areas at-risk of periodic inundation (storm surge). Of the 632 Atlantic coast tidal stations with full tidal data, 208 maintain data on HOWL, resulting in some value uncertainty in the base year surface.
The same process was used to create the HOWL surface as was used in creating future sea level surface models. For example, in the table above from the Washington, Potomac River tidal station, the actual ground elevation of HOWL is 2.943 meters (see section 2.1 for further explanation of process) and one of the scenarios for the increase in sea level is 48.5 cm (0.485 m). The addition of the estimated increase to the base year provides a sum of 3.428 meters. This process was repeated for each tidal station and sea level rise scenario and a new surface model TIN created for a total of 9 HOWL surface models.
2.4 - Identifying Areas of Concern
The areas of concern are the areas that will be regularly inundated, the areas falling between the current MHHW and the projected sea level under each sea level rise scenario; and that are at-risk of periodic inundation, areas that fall between projected sea level and the projected HOWL under each SLR scenario.
These areas were produced by using a 3D geographic information system tool that compared the surfaces created in the previous steps to Digital Elevation Models (DEMs) produced by US Geological Survey (USGS) for the National Elevation Dataset (NED). These have a horizontal grid size of 30 meters. Please see section 2-5 for more about the DEM data used in this study.
These DEMs were then resampled to a 5 meter resolution using a bilinear interpolation to prevent "terracing" that occurs at such coarse scales as the 30 meter resolution.17 This function smoothes out the DEM and provides interpolated elevation data between the cells.
Figure 2-4: Areas of Concern
The surface models for all scenarios were then compared to the DEMs to determine where the surface models were above the elevation of the DEMs. For each sea level rise scenario, this comparison identified areas that would be regularly inundated or at-risk of periodic inundation due to storm surge. The results are created as polygon features.
2.5 - About the Digital Elevation Models
The Digital Elevation Models (DEMs) used in this study are a product of the USGS and are know as the National Elevation Dataset (NED). The USGS DEMs were, at the initiation of this study, "the highest-resolution, best quality elevation data available across the United States."18 Additionally, the NED was at the time of this study the only consistent elevation dataset publicly available for the entire study area. The DEMs were downloaded from the US Department of Agriculture's (USDA) Geospatial Data Gateway.19 North Carolina, Virginia, Maryland, Washington DC, Delaware, Pennsylvania, New Jersey, and New York DEMs are in UTM (Universal Transverse Mercator) zone 18, while Georgia, South Carolina, and the Atlantic Coast of Florida were all in UTM zone 17. The NED has full coverage of the United States at 30 meter resolution, and partial coverage of the United States at 10 and 3 meter resolution.20 The 30 meter resolution was used in this study because it was the only dataset that covered the entire study area. A need for consistency in methodology overrode the potential for higher detail in certain areas.
Although this was the most appropriate data set for this study, there were some issues identified. These were primarily either due to the process used to create the DEM, or the quality of the "best quality elevation data" source data that went into making the DEM. Bordering DEMs can have vastly different quality, depending on how or when the DEM or source data was updated. See Figure 2-5 for an example of the edge variations. USGS DEMs have been produced using several very different methods, the accuracy of which has progressively improved over time. Outdated techniques for creating large scale DEMs, which are no longer used, include digitizing topographic maps and then processing them into a grid format, creating orthophotos and manually profiling them (which were notorious for creating artificial ridges and valleys throughout the DEM, making it look as if it has stripes), or using stereo plotters to create contours from stereo photographs. Currently, DEMs are created from interpolating digital line graph data reprocessed into a raster format. In some cases the edges of two adjacent DEMs are inconsistent. Often this has to do with temporal variations in the data collection. In one example in southern Florida, an artificial cliff runs the entire length of the two DEMs. This was caused by a significant difference in source data ages and collection and processing methodology. Another issue encountered involved DEMs that appear "speckled". This may be a result of down sampling a DEM. This would be done in order to make the higher resolution DEM match the pixel size and resolution of the larger dataset.21This was the most prominent issue in the Virginia portion of our study area - an example of this can be found in ‘speckling'. On the bottom image (FL) there is a visual difference between the quality of the left and the right sides.
While these issues are important to understand, they actually affect only a small portion of the overall data. In fact, these issues were a factor in only 36 out of the 1098 total DEMs in the study area - or about 3% of the DEMs. And since the areas of inundation do not always cover the entire DEM area, the area affected by these issues is actually less than 3%.
'speckling'. On the bottom image (FL) there is a visual difference between the quality of the left and the right sides.
2.6 - Identifying Potentially Affected Transportation
Once the areas of concern polygons were created, they were overlaid upon the transportation network data to identify potentially affected transportation infrastructure.
The data used in this analysis include:
1:100K scale Road data from the National Highway Planning Network (NHPN)22 including. The study included:
- Interstate Highways
- Non-Interstate Principal Arterials (hereafter referred to as Principal Arterials)
- Minor Arterials (Including all Rural Minor Arterials, but not the Urban Minor Arterials)
- National Highway System (NHS)23
1:100K scale Rail data from the Federal Railroad Administration
1:100K scale Airport boundaries and runway areas from TeleAtlas24
1:100K scale Port boundaries digitized from DOQQs25 for the land boundaries and the MHW line for the water boundaries. Ports included are part of the following port authorities:
- The Port Authority of New York & New Jersey
- The South Jersey Port Corporation, NJ
- The Philadelphia Regional Port Authority, PA
- The Delaware River Port Authority
- The Port of Baltimore, MD
- The Virginia Port Authority
- The North Carolina State Ports Authority
- The South Carolina State Ports Authority
- The Georgia Ports Authority
- The Florida Ports Council (Atlantic coast ports only)
The roads and rails were overlaid with the areas of concern to identify the linear distance in kilometers affected within each scenario. The airports, runways and port areas were also intersected with the areas of concern to identify the area in acres affected under each scenario. A portion of the bus public transit system impacts could be reflected in the results for roads, and the commuter rail system results are reflected in the results for rail. While heavy rail and light rail public transportation systems such as subways and metros were not assessed, systems in areas that are regularly inundated or at risk to storm surge could also be affected.
Since the elevations from the DEMs represent the actual ground elevation, this study did not account for situations where infrastructure is artificially elevated. However, the results in this study are still relevant in those areas. For example, a highway with a high bed is indicated as inundated in this study. While the road itself may not be underwater, the bed, which is inundated, was not likely designed to be permanently underwater and thus must still be considered for mitigation.
2.7 - Statistics Calculations
From the analysis in the previous steps, statistics at the county and state level were created for each scenario. For each scenario the statistics include:
- Kilometers of Interstate Highways potentially impacted
- Kilometers of Non-Interstate Principal Arterial roads potentially impacted
- Kilometers of Minor Arterial roads potentially impacted
- Kilometers of National Highway System facilities potentially impacted
- Kilometers of Railroads potentially impacted
- Total acres of Land potentially impacted
- Acres of Airport Property potentially impacted
- Acres of Airport Runways potentially impacted
- Acres of Port Property potentially impacted
The statistics tables include both regularly inundated and at-risk land areas. These are mutually exclusive, meaning the areas at-risk do not also include regularly inundated areas. The sum of these two fields equals the total land area potentially impacted by the effects of SLR and storm surge under the 59 cm SLR scenario. For example, in the table below, the total area for the 59 cm scenario is the sum of the regularly inundated or permanently flooded area, 236,581 acres, and the area at-risk to temporary flooding due to storm surge