Increasing sea level has the potential to place important infrastructure we rely on every day at risk, yet we lack good data to make decisions on what to do, when, and with what priority. The objectives of the research were to develop a method for estimating the time scales for various increments of sea level rise (SLR) throughout the 21st century, develop an accurate methodology for predicting impacts of SLR at the local level, and develop recommendations as to how existing data sources can be utilized to identify infrastructure vulnerable to SLR. The methodology was applied to southeast Florida using data from the Florida Department of Transportation, the United States Geological Survey, the National Oceanic and Atmospheric Administration and other sources, integrated with low resolution light detection and ranging data, topographic data, and aerial photographic maps to identify potentially vulnerable infrastructure. Overlaying high resolution light detection and ranging data onto a base map enabled creation of mapping tools to evaluate potentially vulnerable infrastructure. Using these recommendations, a protocol was developed to use groundwater adjusted models in southeast Florida which indicated potential underestimation of the risk of damage to public infrastructure and private and public buildings.

BACKGROUND

During the past 100 years, an increase in sea level has been observed (Bloetscher et al. 2013), which is expected to have significant consequences for coastal areas where the combination of sea level rise (SLR) and population growth makes it essential to continue improving flood management strategies (Parkinson 2010; Zhang et al. 2011; NFIP 2011; Schmidt et al. 2011; Warner & Tissot 2012). Various researchers have already noted impacts on coastal and island environments (Church et al. 2001; Riggs 2001; Nicholls 2004; Murley et al. 2008; OECD 2008; Poulter & Halpin 2007; Purvis et al. 2008; Coombes & Jones 2010; Frazier et al. 2010; Parkinson 2010; Zhang et al. 2011; NFIP 2011; SFRCC 2011).

The purpose of SLR vulnerability modeling is to explore future vulnerabilities of infrastructure and buildings and facilities on public and private property due to the increase in sea level by predicting how areas with low elevations may be affected by inundation from the ocean directly, from rising groundwater levels, and inundation from the inability of inland areas to drain. One issue not considered in prior SLR modeling efforts is the impact of soil storage capacity and groundwater levels on the potential to flood or damage infrastructure as they primarily focus on coastal regions and average mean tides (or mean high tides). These prior efforts assume a bathtub approach (which assumes that flood and groundwaters will stabilize horizontally to match the elevation of the ocean tides) to determine vulnerability. This scenario is used by many governmental organizations due to the ease of data acquisition and model creation. The main disadvantages of this type of model is that it does not consider urban water control infrastructure such as dikes and canals (Inglesias-Campos et al. 2010), or that groundwater levels can lead to underestimation of inundation because they do not identify low-lying inland areas that might flood at an earlier time than areas along the coast as a result of higher groundwater tables. A modified bathtub model considers more than just static elevation to determine SLR vulnerability.

Groundwater levels increase as one moves away from the coast. The importance of the groundwater table in the model is that it is responsible for determining the soil storage capacity (Gregory et al. 1999), which is related to the ability of local soils to absorb flood waters. As soil storage capacity is lost due to rising groundwater levels, local and areal flooding increases. As a result, projecting groundwater levels will indicate infrastructure with a greater risk for flooding, and more rapid failure of roadway bases and buried infrastructure. As a result, water, sewer, stormwater, and transportation infrastructure in low-lying inland areas may be compromised faster due to the loss of soil capacity. The intent of this project is to create and apply a higher accuracy SLR vulnerability model that incorporates the use of an additional groundwater surface elevation for better predicting vulnerable land and infrastructure.

Table 1

Total vulnerability predictions of land area in Miami-Dade and Broward Counties (square miles)

ModelCurrent1 ft2 ft3 ft
Bathtub model 133 172 229 326 
Groundwater adjusted April 50th% 180 (+35%) 238 (+38%) 324 (+41%) 455 (+40%) 
Groundwater adjusted October 50th% 236 (+77%) 315 (+83%) 441 (+93%) 601 (+85) 
Groundwater adjusted October 100th% 337 (+154%) 573 (+233%) 647 (+183) 775 (+138%) 
ModelCurrent1 ft2 ft3 ft
Bathtub model 133 172 229 326 
Groundwater adjusted April 50th% 180 (+35%) 238 (+38%) 324 (+41%) 455 (+40%) 
Groundwater adjusted October 50th% 236 (+77%) 315 (+83%) 441 (+93%) 601 (+85) 
Groundwater adjusted October 100th% 337 (+154%) 573 (+233%) 647 (+183) 775 (+138%) 

The objectives of this paper are to: (1) outline a method for estimating the time ranges for various increments of SLR throughout the 21st century based upon recent literature forecasts of SLR in 2100; (2) develop an accurate methodology for predicting impacts of SLR at the local level in low-lying southeast Florida; and (3) develop recommendations as to how existing data sources can be utilized to identify infrastructure vulnerable to SLR.

METHODOLOGY

The means to assess what infrastructure is vulnerable to SLR requires detailed topographic information. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Previous approaches to modeling inundation from simulated SLR have been limited by coarse-resolution elevation datasets (surveys, field spot elevations, United States Geological Survey (USGS) maps) as opposed to high resolution electronic imagery (Park et al. 1988; Moorhead & Brinson 1995; Titus & Richman 2001; Duke et al. 2003; Nicholls 2004; Titus & Wang 2008). However, communicating the importance of SLR to local entities requires better data (Small & Nicholls 2003; Marbaix & Nicholls 2007; Poulter & Halpin 2007). Low resolution light detection and ranging (LiDAR) is available in many areas, but the coarse vertical definition (±2 feet) is not useful for coastal areas where inches matter.

Duke et al. (2003) showed that while higher resolution elevation data represent a significant advance for modeling SLR impacts, there can be a large variability in inundation estimates depending on the horizontal fit of the raster data (Poulter & Halpin 2007). High resolution elevation data are needed for investigating the influence of topographic complexity on landscape processes, including drainage canals and levees. Due to the narrow and compact organization of drainage channels, they may not always be detected in raster elevation datasets at less than high resolution (Duke et al. 2003). Gesch (2009) compared digital elevation models (DEMs) used in previous SLR vulnerability assessments such as USGS global 30-arc second GTOPO30 (∼1 km horizontal resolution), Shuttle Radar Topographic Mission (∼90 m horizontal resolution), and National Elevation Dataset (30 m horizontal resolution) with LiDAR (3 m horizontal resolution) to determine that LiDAR DEMs provide improvements to mapping vulnerable lands due to their high horizontal resolution and vertical accuracy. Zhang (2011) also examined the effect of horizontal resolution on identifying individual properties vulnerable to SLR by comparing 30 and 5 m LiDAR DEMs to determine that LiDAR DEMs ≤ 5 m horizontal resolution are necessary. The availability of higher resolution LiDAR is among the factors that have led to an increased belief and trust in using LiDAR as a means for assessing vulnerable infrastructure and developed public and private properties (Franklin 2008).

The geospatial data user community has recognized the usefulness of LiDAR as a means to provide the highly detailed and accurate topographic data needed for SLR projections, which has increased interest in developing a national LiDAR database (Stoker et al. 2007, 2008). If such databases existed, better definition of the potential SLR impacts in the United States could be realized. The increasing availability of high quality LiDAR in coastal areas allows for improved assessments to be done over more areas and integrated into national datasets (Gesch 2007).

The highest resolution LiDAR available is ±7 inches (0.2 m). Such high resolution LiDAR datasets are available for coastal Florida. The LiDAR data format used was the American Standard Code for Information Interchange (ASCII) which is easily handled by ArcGIS software. The ASCII format comprises the raw LAS LiDAR data type format, translated into a geographically referenced X, Y, Z global coordinate plane system (FDOT 2012). Of the different topographical data repository sources, the National Oceanic and Atmospheric Administration (NOAA) offered the data natively in ASCII format.

The NOAA LiDAR was used to develop surface topography. Of note is that over 50% of the developed areas of southeast Florida lie below 5 ft NAVD88 and the highest points are under 15 ft NAVD88 – the area is essentially flat and low. The Central and South Florida project (1930s–1960s) was designed to use 1,800 miles of regional canals to drain the aquifer/swamp to permit development. The dikes on the western edge of the developed areas were installed where the water could move by gravity to the ocean based on the 1929 datum (note sea level has risen 9 inches since that time which compromises the high season tide drainage efforts). As a result, the contour of groundwater from the dike to the coast is controlled by the canal gradients. The canals control groundwater levels (and there is minimal ability to stage water levels higher than high tide since all water drains to the canals through porous sand and then the oceans), which is at odds with the bathtub results of others.

Instead, what was found was that the critical groundwater elevation seek high tide along the coast and then gradient upward as one moves inland. To generate the groundwater layer, the data from a series of USGS monitoring wells was used. In order to be considered, the gauging station had to have a minimum of 35 years of continuous data. Only stations currently in use were considered to ensure the data incorporated the current time period. An issue that arose was the wide swings in groundwater elevation with season in the groundwater monitoring wells. As can be seen in Figures 1 and 2, the groundwater levels change throughout the season, in part due to the porous nature of the sand and underlying limestone (porosity of both is 15–20%). Both have significant hydraulic conductivity. It is the seasonal high tides in October which produce the most flooding.

Figure 1

Frequency analysis of lowest and highest mean groundwater level for south Florida.

Figure 1

Frequency analysis of lowest and highest mean groundwater level for south Florida.

Figure 2

Monthly mean extremes difference in groundwater surface for southeast Florida.

Figure 2

Monthly mean extremes difference in groundwater surface for southeast Florida.

Based on the results of the database and large seasonal swings of the groundwater surface, three separate scenarios were determined to be considered to encompass the effects of different SLR intervals. The determined levels consisted of the 50th percentile monthly average values for April and October which showed the low level and high level extremes, respectively. Note that where muck underlies the soil (remnant mangroves), the flooding will actually be more lengthy and likely deeper than the model suggests because in those areas the groundwater is unlikely to absorb the runoff as quickly.

These data points were used to develop the groundwater surface elevation layer. Various interpolation methods were considered to determine the surface that produced the best results including inverse distance weighting, ordinary kriging, co-kriging, and kernel density functions. The resulting interpolation that produces the best performance measures was the ordinary kriging, which was then applied to the model as the groundwater surface elevation. A correction to groundwater data had to be completed to convert hydraulic head values from NGVD 1929 to NAVD 1988, to match the terrestrial elevation dataset. The adjusted groundwater surface protocol was applied to Miami-Dade and Broward Counties. Both counties have high vulnerability due to 50% of the land surface being below elevation 5 ft (1.6 m) NAVD88.

The final model variant was an extremes model based on the highest median monthly value for the month of October, which is the historical highest tide. The mean tide and the highest October tides were 1 ft (0.3 m) different.

Future groundwater surface elevation models were created by adding a specified height to the existing groundwater table since there is no current ability to isolate groundwater from the canal controls that drain the aquifer to the ocean. The assumption made was that an increase in SLR would shift the starting point of the hydrological gradient, the ocean coast interface, by the same vertical dimension along the entire gradient line. The final inundation model was created in a geographic information system (GIS) by subtracting the groundwater surface model from the digital elevation model, as shown in Figure 3, with the difference in elevation being the soil storage capacity of water. One caveat that demands future attention is that the current results include current water control management strategies since the initial groundwater table results were derived from historical values. The future projection results can vary depending upon future water control management practices.

Figure 3

Soil storage capacity example.

Figure 3

Soil storage capacity example.

The initial roadway vulnerability assessment was conducted by overlaying the roadway network on top of the adjusted SLR inundation models to visualize areas of vulnerability. To analyze the roadway data, G, the roadway centerlines were converted into 50 ft (15 m) raster cells. A validation comparison between the actual elevation and SLR model was conducted at multiple random locations and tested against the established performance measures by comparing the value in the raster cell similar to that of the base soil storage capacity map for the region.

The next step of the project was to reconvert the roadway cells to polylines to permit ease in the summation of inundated segments easier to visualize and produce summary statistics using ArcGIS. The roadway system was used as a surrogate for all other public infrastructure since most of the latter is located in roadway rights-of-way.

The current condition was developed as a baseline (mean high tide). The baseline includes peak seasonal water table (October after the wet season and corresponding to the seasonal high water table) and April (corresponding to the end of the dry season and seasonal low tides). The next step was to develop results for the 1 ft (0.3 m) influenced bathtub which were created by changing the symbology within ArcGIS, followed by 2 (0.6 m) and 3 ft (0.9 m) SLR scenarios. Drilldown efforts were used to identify ‘potentially vulnerable,’ and thereafter ‘vulnerable’ infrastructure which comports with directives from the United States Army Corps of Engineers that any coast or near-coast projects must include consideration of SLR. Similarity, transportation and utility agencies should include the impact of SLR into all long-range planning. It is also important to incorporate adaptive management processes into the planning as more information becomes available.

RESULTS AND DISCUSSION

The bathtub approach assuming current conditions, with mean high tide at 2 ft (0.6 m), is shown in Figure 4(a). Using the groundwater adjusted model, the area vulnerable to inundation is 133 square miles. This was compared to the mean high tide map for the dry season (April 50%) using the groundwater adjusted model (Figure 4(b)). The vulnerable land area is 35% higher than the bathtub model suggests. The groundwater adjusted map for the wet season mean tide (October 50%) is 236 square miles or 77% greater than the bathtub projection, including coastal inundation areas as well as western flooding (Figure 4(c)). With the highest median October tides, the amount of vulnerable lands increases by 154% over the bathtub model (Figure 4(d)). There are more areas both west and along the coast that incur flooding. The coastal areas are periodic as a result of tides, but the inland areas are where extensive storm water programs have or will occur to compensate for lost soil storage capacity. Hence, there is far more vulnerable land identified using the groundwater adjusted model than the bathtub model currently used by policy-makers. Flooding complaints are mostly a summer and fall phenomenon which is indicated by the figures. These results make sense in that when the water table is low the effects of the groundwater surface do not exhibit an influence on infrastructure vulnerability.

Figure 4

(a) Bathtub results model (current condition); (b)–(d) soil storage capacity current condition.

Figure 4

(a) Bathtub results model (current condition); (b)–(d) soil storage capacity current condition.

SLR planning for southeast Florida has suggested planning by 2060 and 3 ft (0.9 m) by around 2100. Figures 5(a)5(d) reflect the results of the 3 ft (0.9 m) rise for the same scenarios as Figure 4. The results for the adjusted bathtub further increase the amount of vulnerability given 3 ft of SLR, but substantial differences occur with the groundwater adjusted map, the mean October tide and the highest October tides. Using the groundwater adjusted model, the area vulnerable to inundation is 326 square miles (844 sq km). This was compared to the mean high tide map for the dry season (April 50%) using the groundwater adjusted model (Figure 5(b)). The vulnerable land area is 40% higher for the April 50% value than the bathtub model suggests. The groundwater adjusted map for the wet season mean tide (October 50%) is 601 square miles (1,557 sq km) or 85% greater than the bathtub projection, including coastal inundation areas as well as western flooding (Figure 5(c)). With the highest median October tides, the amount of vulnerable land increases by 138% over the bathtub model (Figure 5(d)).

Figure 5

3ft SLR vulnerability using soil storage capacity.

Figure 5

3ft SLR vulnerability using soil storage capacity.

Under all scenarios, in particular the western portion, the study region has a higher predicted vulnerability to SLR than the bathtub model predicts. The results further illustrate that SLR vulnerability is not just a coastal feature for the study region in that the inundation is shown to move from inland areas towards the eastern coast of Florida. A summation of the increasing vulnerability in terms of square miles of developed area under the SLR scenarios, as compared to the bathtub model are tabulated in Table 1.

These areas can be related to roadways. Using the same scenario, the raster cells for roadways were tabulated to numerically quantify the increasing amount of predicted roads to be inundated under the different scenarios of SLR. Table 2 outlines the summation of the increasing vulnerability in terms of linear miles. Table 2 indicates that the roadway miles under the base condition for April tide is 40% higher, and for the peak October tides 177% higher than the current bathtub models suggest. Likewise, the values under the 3 ft (0.9 m) scenario were 39 and 181% higher, respectively, using the groundwater adjusted model. State roadways are used as evacuation routes and movement of economic goods and services. These major roads are critical to the economic health and well-being of southeast Florida. Table 3 shows that for state roadways, which include interstates and beach access highways, there is no significant impact (less than 0.6 mile (1 km) with 3 ft (0.9 m) of SLR. Yet the values for the 3 ft (0.9 m) scenario were 67.5 miles (100 km) in comparison, a 100-fold increase in investment, using the groundwater adjusted model.

Table 2

Projected miles of roadways inundated

ModelCurrent1 ft2 ft3 ft
Bathtub model 37 88 133 172 
Groundwater adjusted April 50th% 70 123 (+40%) 180 (+35%) 239 (+39%) 
Groundwater adjusted October 50th% 128 180 (+104%) 236 (+77%) 315 (+83%) 
Groundwater adjusted October 100th% 177 244 (+177%) 337 (+153%) 483 (+181%) 
ModelCurrent1 ft2 ft3 ft
Bathtub model 37 88 133 172 
Groundwater adjusted April 50th% 70 123 (+40%) 180 (+35%) 239 (+39%) 
Groundwater adjusted October 50th% 128 180 (+104%) 236 (+77%) 315 (+83%) 
Groundwater adjusted October 100th% 177 244 (+177%) 337 (+153%) 483 (+181%) 
Table 3

Summary results for Florida Department of Transportation (FDOT) highways at three SLR scenarios compared to bathtub model

Model typeTotal distance (mi)Currently (mi)1 ft SLR (mi)2 ft SLR (mi)3 ft SLR (mi)
Bathtub 887 0.13 0.56 
Groundwater adjusted October – 50% 887 10.3 21.2 39.8 67.5 
Model typeTotal distance (mi)Currently (mi)1 ft SLR (mi)2 ft SLR (mi)3 ft SLR (mi)
Bathtub 887 0.13 0.56 
Groundwater adjusted October – 50% 887 10.3 21.2 39.8 67.5 

The results for the models that incorporate the loss of soil storage capacity created by rising groundwater levels indicate that the persistent flooding of roadways will start in the western portion of the study region farthest from the coast. The results for the right-of-way roadway network using the bathtub model approach in Tables 1,23 indicate the current bathtub planning models significantly underestimate the amount of vulnerable infrastructure which is a potential problem for infrastructure planners. The amount of additional vulnerable land and infrastructure suggest that while most of the focus is on the coast, more focus is needed on the inland areas which will impact a large percent of the land area and roadway network. Western vulnerability is minimized in the bathtub models.

CONCLUSIONS

The results of this effort indicate that the inclusion of the groundwater table into the calculations of vulnerable infrastructure due to SLR will help policy-makers identify priority areas beyond coastal regions. The concern is that the exposure to SLR may be far greater than expected, meaning greater costs. Since much of the infrastructure investments will come locally, there is a need to insure that local officials do not accelerate infrastructure construction that will not be used for another 30 years, while insuring that the planning is in place to address adaptation needs before a crisis occurs. Given that SLR occurs slowly, there is time, but that time cannot be squandered.

As a result of this effort, it is recommended that local and state governments consider SLR projections and impacts on their infrastructure planning that include groundwater adjustment mapping. This will enable them to develop management strategies in planning, design, construction, and maintenance of infrastructure to insure that adaptation strategies can be implemented at the appropriate time and location. The current planning may focus more on coastal flood issue than inland areas otherwise thought to be ‘safe’ from SLR effects. Identifying vulnerability will help support infrastructure prioritization processes and public education programs. The results of the project have shown that local entities can use readily available topographic and ArcGIS mapping tools to evaluate infrastructure vulnerability; specifically, additional data layers such as groundwater, storm water retention/detention areas, USGS soils maps to consider information that can effect flooding, inundation, and damage to the infrastructure. A big issue is that regulatory agencies are not using all the available data layers to create better models that incorporate the plethora of information already available.

The project is readily transferrable to other coastal jurisdictions where groundwater level data are available and are controlled by water bodies or aquifers that drain directly to the ocean. Where there are means to alter natural groundwater patterns with control structures, adjustments must be made. Also, the results may suggest such improvements which would alter the scenarios. Southeast Florida is currently looking at such options.

The protocol for identifying vulnerable infrastructure applies to water, sewer, transportation, and storm water infrastructure, as they are all related as a result of being generally found in transportation corridors. The GIS tools are readily adaptable for other purposes. Beyond identification of vulnerable infrastructure, including water, sewer, and roadways, other aspects such as buildings, bridges, and ports should be investigated. In addition, the next step could be to incorporate surge impacts as temporal events onto the current GIS results to evaluate critical storm surge vulnerability.

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