Assessing Sea-Level Rise Impacts: A GIS-Based Framework and Application to Coastal New Jersey

Transcription

1 Coastal Management ISSN: (Print) (Online) Journal homepage: Assessing Sea-Level Rise Impacts: A GIS-Based Framework and Application to Coastal New Jersey James E. Neumann, Daniel E. Hudgens, John Herter & Jeremy Martinich To cite this article: James E. Neumann, Daniel E. Hudgens, John Herter & Jeremy Martinich (2010) Assessing Sea-Level Rise Impacts: A GIS-Based Framework and Application to Coastal New Jersey, Coastal Management, 38:4, , DOI: / To link to this article: Published online: 14 Jul Submit your article to this journal Article views: 488 View related articles Citing articles: 17 View citing articles Full Terms & Conditions of access and use can be found at Download by: [Bowdoin College] Date: 21 November 2016, At: 10:58

2 Coastal Management, 38: ,2010 Copyright Taylor & Francis Group, LLC ISSN: print / online DOI: / Assessing Sea-Level Rise Impacts: A GIS-Based Framework and Application to Coastal New Jersey JAMES E. NEUMANN, 1 DANIEL E. HUDGENS, 1 JOHN HERTER, 1 AND JEREMY MARTINICH 2 1 Industrial Economics, Incorporated, Cambridge, Massachusetts, USA 2 Climate Change Division, U.S. Environmental Protection Agency, Washington, DC, USA The impact of sea-level rise on coastal properties depends critically on the human response to the threat, which in turn depends on several factors, including the immediacy of the risk, the magnitude of property value at risk, options for adapting to the threat and the cost of those options, and in some cases, land-use or regulatory restrictions that apply to the property. This article reports on a new effort to model the response to and economic impacts of sea-level rise on coastal properties using a spatially comprehensive Geographic Information System (GIS)-based modeling approach that considers each of the aforementioned factors. The approach is applied to a multi-county section of New Jersey s Atlantic coast to provide estimates of the costs of protection, elevation, and abandonment. The new model yields impact estimates higher than prior estimates, resulting from recent increases in the value of coastal property at risk, the spatially comprehensive nature of the approach, and our use of more recent and accurate elevation data. The approach will ultimately yield two types of results: national-level estimates of the benefits of reducing sea-level rise through control of greenhouse gas emissions; and local-level results assessing management actions that could facilitate adaptation to sea-level rise risks. Keywords climate impact assessment, coastal behavior model, coastal property, GIS, sea-level rise Introduction Coastal areas are highly vulnerable to the impacts of climate change. Current research suggests that climate change will accelerate the rate of sea-level rise along much of the U.S. coastline (Meehl et al., 2007), resulting in flood damages, erosion, wetland inundation, and other ecological losses (Field et al., 2007; CCSP, 2009). These impacts will interact with existing coastal stressors, such as development and pollution, to reduce the overall resiliency of coastal systems (Field et al., 2007; Karl et al., 2009). We gratefully acknowledge the financial support of the U.S. Environmental Protection Agency s (EPA s) Office of Atmospheric Programs (Contract #GS-10F-0224J). Technical contributions and project support were provided by Gary Yohe, James Titus, and Ben DeAngelo. The authors also acknowledge the assistance of the following individuals: Gaurav Sinha, Caroleen Verly, and participants at an October 2008 workshop of the Energy Modeling Forum Uncertainty Subgroup. Address correspondence to James Neumann, Industrial Economics, Incorporated, 2067 Massachusetts Avenue, Cambridge, MA 02140, USA. jneumann@indecon.com 433

3 434 J. E. Neumann et al. Much work has been done in the United States to assess the vulnerability of coastal areas to sea-level rise, and the ready availability of digital elevation data means this literature should increase with time, but only a handful of studies incorporate the ability of coastal managers and landowners to adapt to sea-level rise. The recent U.S. Global Climate Research Program s review of the sensitivity of coastal areas to sea-level rise is notable for its thorough review of both property and natural resources at risk from this threat, and it also provides information on the options for responding and adapting to sea-level rise, but it does not combine vulnerability with adaptation in a quantitative assessment of impacts (CCSP, 2009). Titus et al. (2009) provide a systematic assessment of coastal managers plans for development in coastal areas, but do not assess the cost of these plans. Heberger et al. (2009) evaluate a wide range of resources vulnerable to sea-level rise and storm surge in California using up-to-date data and sophisticated spatial analysis, and also provide an approximation of the costs of adaptation, but they do not combine vulnerability and adaptation cost assessments to estimate the net quantitative impacts. Amorecompleteunderstandingoftherisksposedbyclimatechangeandthebenefitsof avoiding damages to coastal areas requires quantification and valuation of potential impacts. This information can serve multiple purposes. First, valuation and incorporation of potential climate change impacts on coastal communities and ecosystems will help inform the design of climate change policy. Representation of these impacts in policy discussions may help reduce or minimize future costs, inform decisions to balance investments in mitigation and adaptation, and identify conditions that lead to the most dangerous and costly impacts. Second, land use planners responsible for protecting coastal development and natural systems will increasingly rely on information regarding resources and decisions that are most vulnerable or sensitive to climate change. In response to rising sea levels, coastal planners and homeowners are likely to protect their property by elevating structures, constructing seawalls, nourishing beaches, and using other methods. Such an investment in protecting shorelines will likely impose substantial costs on property owners, local municipalities, and state and federal programs. Modeling tools designed to estimate the value of resources at risk and the costs associated with protecting land from these impacts can help managers make decisions that promote sustainability, reduce overall costs, and improve coastal resiliency. While effective adaptation to the impacts of climate change can greatly reduce the eventual costs, it requires careful advance planning. In this article, we present a new analytic framework for estimating human response to the threat of sea-level rise (SLR) and the economic impacts of SLR on coastal property, with potential applications to policy and local land-use decisions. This framework employs Geographic Information System (GIS) software to structure and overlay available data (including coastal elevations, parcel-level property value data, and land-use and zoning categories) using a 150-meter grid cell network. The model is distinctive for its comprehensive geographic coverage, rapid run-time, and the flexibility of inputs. Through simple Microsoft Access-based interfaces, users can select from preloaded regions (e.g., Ocean County, NJ) and SLR scenarios as well as revise default response and unit cost parameters. The tool then models a response to the SLR threat over time, and reports estimates of the expected response mode (e.g., armoring or abandonment), property at-risk, property damages, and costs of the adaptation response in both graphical (map and chart) and tabular form. The results support other work in this area which suggests that, under virtually all SLR scenarios, there is a strong private economic incentive to fortify shorelines against inundation risk, through seawall/dike construction and beach nourishment (Yohe et al., 1995; Nicholls & Tol, 2006), but that significant acreage is also likely to be abandoned. 1 This strong private incentive to protect with hard structures or nourishment may, in some

4 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 435 areas, put ecological resources at risk for example, hard structures landward of current wetland resources could jeopardize wetlands ability to accrete or migrate as sea levels rise. 2 Shoreline armoring can also eventually eliminate ocean and estuarine beaches, mudflats, and very shallow open water areas by blocking their landward migration (CCSP, 2009). We report preliminary evidence from application of this model that suggests a policy that required accounting for the ecological consequences of widespread armoring, based on direct consideration of the ensuing social costs associated with putting ecological resources at risk, might be sufficient to alter the armoring response in some areas, from armoring to amoreecologicallybenignalternative. Model Overview The overall framework of the model is presented in Figure 1. The basic structure involves arraying relevant input data and constructing a spatial geo-database on a 150-meter grid cell frame. The analysis and aggregation modules then access the geo-database, along with a series of user-defined input parameters, such as SLR scenario and the cost of armoring or beach nourishment, and estimate the response to SLR. Decision-making for the response modeling can be based either on an optimal protection algorithm, which effectively approximates benefit-cost analyses for each grid cell and, in some cases, neighboring cells, or a user-defined mapping of planning, zoning, and/or land-use categories to a specific response category (protect or no-protect). Some elements of the framework, particularly the optimal response mode, rely extensively on earlier research by Yohe and collaborators assessing economic impacts for a INPUT DATA SPATIAL ANALYSIS OF STUDY AREA ANALYSIS (CONDUCTED ITERATIVELY OVER TIME) PROPERTY CHARACTERIZATION VULNERABILITY ASSESSMENT Elevation Data Tide Gauge Data Land Use & Zoning Shoretype (e.g., Bay/Ocean; Beach) Property Value Estimate Elevations Relative to Spring High Water Characterize Property and Shores Quantify Property Values Over Unit of Analysis Identify Threatened Properties Estimate Protection Costs Estimate Value of Property When Threatened Site Specific Protection / Abandonment Determination PARAMETERS Sea Level Rise Rate Unit Cost of Protection Strategies RESULTS AGGREGATION Evaluate Overall Study Area Costs Figure 1. Overall model structure.

5 436 J. E. Neumann et al. Q1: Is cell elevation less than relative sea level in that year? Yes Q2: Is cell land and structure value more than protection cost (capital plus O&M)? Yes Q3: Are any neighbors threatened? Yes Q4: Is cell plus neighbor cells land and structure value more than cell plus neighbor protection cost? Yes No No No No Not threatened Abandon Elevate Armor/Nourish Cost = land + structure value Cost = elevation cost for structure and some surrounding land Cost to armor = capital + annual O&M Cost to nourish = incremental sand cost, with new sand each decade Figure 2. Decision tree for modeling the response to SLR. sample of 30 coastal U.S. sites (Yohe et al., 1995, 1996). The Yohe model was originally developed to respond to a gap in the literature on SLR impacts; in the mid-1990s, estimates for the cost of holding back the sea (Titus et al., 1991), and of not holding back the sea (Yohe, 1990) had been developed, as had an estimate that applied a decision rule for where those two alternative responses might be applied, but there were no existing estimates that reflected an optimal response (see Neumann et al., 2000 for more details). The optimal response approach is based on a simplified benefit-cost analysis of protective structures and beach nourishment relative to a retreat response, as illustrated in Figure 2. Where the cost of protective measures is less than the benefit of avoided property value loss, the impact of SLR is estimated as the capital cost of construction plus on-going maintenance costs. Where the cost is more than the benefit, retreat/abandonment is the estimated response, and the impact of SLR is lost structure and land value. For computational efficiency, in the Q2 step of the process we use a simplified estimate of the discounted cost of capital and maintenance once a cell is marked for protection, however, we do a more careful calculation of the costs of protection. The benefits side of the benefit-cost calculation uses the land and structure value at the time the property is threatened therefore, consistent with economic theory, we treat property value as, effectively, the discounted cost of the future stream of benefits that would accrue to the landowner if the property was not lost. In some cases, individual cells may warrant armoring or beach nourishment, but neighboring cells may warrant retreat/abandonment. Armoring or even beach nourishment projects are usually not feasible on a scale as modest as 150 meters, the size of a single cell. 3 In these cases, then, the response is not abandonment, as illustrated in Figure 2, but elevation of the structures in the cell and at least some of the surrounding land the details of these and other calculations are described later, and most key parameters, such as the extent of surrounding land elevated, can be altered by the user. The elevation response is less costly than armoring, but elevation probably does not preserve all coastal amenities. Another key feature of our approach, implicit in the response decision tree outlined in Figure 2, is that we also estimate the optimal timing of a response, based largely on the timing of inundation. The timing component is important for any economic calculations

6 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 437 that incorporate a positive discount rate, but timing is also a critically important factor for local planning decisions. As an alternative to the optimal response approach, the model also assesses protection versus abandonment decisions and corresponding costs based on local land use and zoning information. For example, similar to previous analyses of the costs of SLR, users can estimate the cost of protecting only currently developed lands as they become threatened (e.g., Titus et al., 1989, see also Neumann et al., 2000). To assist local planners in the evaluation of impacts associated with currently planned future development, the model also allows for the classification of protection methods to specific zoning categories. The outputs of our model include the areal extent of lands in both protected and unprotected categories; the present value costs of armoring the coast, nourishing beaches, and elevating land and structures; and the value of those parcels not protected that are subsequently inundated. Costs are disaggregated by protection type. Because of the comprehensive geographic scope of our model, we are also able to provide county-wide estimates of linear distance of hard structures and other comprehensive measures of impact. Input Data The left side of Figure 1 lists the key input data. The model makes use of data layers related to elevation, land use and zoning, shoreline type, and property values, as described below. Elevation Data Our elevation data are based on 30 m digital elevation modeling (DEM), calibrated to a zero elevation in the year 2000 as represented by the mean spring high water mark. 4 Estimated tide ranges and sea-level trends by the National Ocean Service (NOS) helped determine the location of spring high water. The authors recognize that DEM data are subject to uncertainty in vertical accuracy. For example, in an analysis of elevations in North Carolina, Gesch (2009) found a linear error of +/ 2.21 meters for 30-meter DEM data versus +/ 0.27 meters for 3-meter LIDAR. However, Gesch (2007) also found the errors inherent in DEM data to be random. Given the regional application for this case study, we feel the use of the 30-meter DEM is appropriate until comprehensive LIDAR data are available. In future work we plan to test the impact of using the finer resolution elevation data. Zoning/Planning and Land-Use Data The New Jersey State government maintains a State Development and Redevelopment plan that designates specific areas for growth, limited growth, and conservation. The most recent version of the plan was finalized in In addition, land use data developed in 2002 and Development and Redevelopment Planning Areas adopted in 2001 are available from the state. As discussed earlier, these designations can be used as an alternative basis for estimating response to SLR risks. Shore Characterization Data The model allows for different armoring strategies to occur along ocean-facing shoreline. One of the armoring methods is beach nourishment. To identify ocean-facing beaches amenable to periodic nourishment as an SLR response strategy, we used land-use data published by the New Jersey Department of Environmental Protection. 5

7 438 J. E. Neumann et al. Property Data We obtained GIS parcel data, attributed with both land and structure assessed values, for ocean-facing counties in New Jersey (Monmouth, Ocean, Atlantic, and Cape May). These data are current as of July 2007 and express real estate values in 2006 dollars. Model Parameters Protection Costs The key economic parameter inputs for the model include: the costs of protection; the discount rate; and the rate of property value appreciation over time. In all cases, the model provides flexibility for user inputs for these parameters, while also providing a set of default values for each of these parameters to use as a reference point. In this version of the model, the capital cost for armoring does not vary by cell, but in subsequent versions we have added that capability maintenance costs do vary by cell, however, as noted below. The costs of protection apply to three alternative protection response strategies: armoring, beach nourishment, and structure elevation. Armoring and beach nourishment response costs were evaluated for Yohe et al. (1995) for that effort, comprehensive literature reviews were conducted, and the results were subsequently peer-reviewed. For this effort we updated cost estimates based on more recent research, where possible. We chose to adopt the same or similar metrics for characterizing costs as the prior work. For structures, the metric is $/ft of protected shoreline for initial installation, and on-going annual maintenance costs for structures calculated as a constant percentage of the initial installation cost. The results of our literature search for more recent information on costs of armoring identified au.s.armycorpsofengineersdatabasethatprovidesbasicdescriptionsandcostsof73 coastal engineering projects, including both structural and beach nourishment projects. The projects have initial installation years from 1950 to 2001, so to standardize the values to a common year (2002) we applied the Army Corps Coastal Civil Works Construction Cost Index System (CWCCIS). Structure project information was available for 41 projects that were initiated between years 1950 and Costs of structural projects ranged between $500 and $1,500 per linear foot. This effectively brackets the $1,000 per linear foot estimate used in the prior Yohe work. We therefore use the $1,000 estimate as our default. Maintenance cost estimates 4 percent annually for back bay sites and 10 percent annually for open ocean sites are consistent with those used in Yohe et al. (1995). For beach nourishment, the prior analysis estimated closure depth at specific sites and then applied the Bruun rule to estimate the size of the beach profile over which beach nourishment would be implemented. We were unable to locate comprehensive data for closure depths and beach profile width for all coastal New Jersey locations in our study site; instead, we developed a generalized beach nourishment sand needs estimate, which reflects variability in shoreline length and SLR but not depth of closure. Our generalized relationship reflects estimated nourishment requirements over five Atlantic sites (including Long Beach Island, NJ) from the Yohe et al. (1995) data; we found fair agreement in incremental sand requirements of just over 3 cubic yds/ft shoreline/cm SLR. Separately, we estimated a default sand cost estimate of just less than $13 per cubic yard, based on high-end results from the sand supply function from Leatherman (1989). These two results are combined in the model to yield total incremental beach nourishment costs attributable to SLR per foot of shoreline per cm of SLR. Both parameters, however, can be altered by the user to reflect more accurate site-specific data, where available. The user can also vary the renourishment cycle to generate alternative estimates our default value for this parameter is 10 years.

8 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 439 Beach nourishment may not be an effective SLR response strategy for larger increments of SLR. Effectiveness is likely to vary from site to site. In past work, we have assumed that beach nourishment is an effective response up to one foot of SLR (Yohe et al., 1995); beyond that point, it must be supplemented by a hard structure. We use that same assumption here as a default value, but allow the user to vary this assumption. There is no guarantee that beach nourishment can be effectively or sustainably carried out at all these sites for our estimated default cost; for example, we would expect that costs for sand will rise as demand increases, that renourishment cycles might grow shorter, and/or that backstop hard structures might be needed sooner than we estimate. Sensitivity analyses using alternative combinations of these parameters are supported by the model framework, however, and could be explored in future work. Property Value Escalation Parameters In prior work, property values were projected to future years using a model for forecasting future property values described in Abraham and Hendershott (1993). Their model suggests that future property values are well characterized as a function of national population and income. There is no indication, however, of how well the function might behave if regional estimates of population and income growth were used. We sought to identify more recent literature to provide a basis for a regional approach. Most of the more recent literature suggests that property values may not vary so much with economic or demographic fundamentals, but be based more on local real estate supply and demand conditions (e.g., Davis & Palumbo, 2008; Davis & Heathcote, 2007; Zabel, 2004). Nonetheless, some recent literature appears consistent with an income elasticity of total property value (land and structure) of about 0.4 to 0.5 (e.g., Zabel, 2004; Capozza et al., 2002). As a result, our default value for projecting property value over time is Our default approach is consistent with historical trends in coastal property value, but an additional concern in estimating future property values is the effect of increasing coastal risk. As these risks increase, proximity to the shore might actually reduce property value over time. The net effect of amenity value and risk implications of proximity to shore are complex and likely site-specific (Bin et al., 2008), one reason we provide flexibility in the property value escalation parameter. Data Processing for Spatial Analysis Because spatial data are the basis of this economic analysis, we implemented our model in adatabaseenvironment.wechosemicrosoftaccessasourdatabasemanagementsystem because it is available to most people, and it can be integrated easily with the popular GIS software suite ArcGIS 9.3. In processing the input data described earlier, we generate polygon-based GIS layers that represent the elevation and assessed economic value of coastal parcels. The input data elements that are required for generating the parcel and shoreline datasets have already been discussed earlier. We now briefly describe how these input data elements are processed for application in the economic analysis model. GIS-Based Data Merging The GIS model uses an ArcGIS Modelbuilder framework to merge each of the necessary data products into a single GIS layer, which provides information on the elevation (relative to spring high water), land use and zoning, land value, and building value for each parcel. The layer s descriptive attributes are used to determine the value of land inundated for each

9 440 J. E. Neumann et al. county under a variety of user-defined scenarios. In this section, we describe modeling assumptions applied to the parcel data to address data gaps. Data limitations forced us to apply assumptions to a portion of the property data for New Jersey because not all parcels had corresponding value data. This was true for most roads and parks but also a few subdivision common areas and residential parcels as well (somewhat evenly distributed across each county). For these areas, we assigned an average per-acre land and building value based on countywide averages. For Monmouth County we had limited parcel coverage and therefore applied per-acre values to all coastal areas absent parcel information, using values from areas where property data were available. Next, we identified shoreline segments along coastal beaches (for subsequent beach nourishment calculations). Where we identified coastal beaches, we manually clipped the shoreline files and added descriptive attributes so that the model could easily isolate the corresponding segments for beach nourishment calculations. Access-Based Analytical Tool The GIS datasets are stored in a Microsoft Access database to enable the economic analysis model to process and report on the economic costs of sea-level rise. The economic analysis model is designed to be customizable through a user interface, such that multiple scenarios can be analyzed to estimate a range of future impacts. Figure 3 illustrates the user interface designed to help the user specify the various model parameters that can be customized for analyzing a wide range of future scenarios. Sea-Level Rise Estimation An important component of the model is the prediction of sea levels in future years. We use the information provided from the MAGICC 6 (Wigley, 2008) and Rahmstorf SLR models to generate estimates of sea levels in future years. Decadal sea-level predictions for several different scenarios are incorporated into our model as an input table. The model interface allows the user to select from a total of 26 scenarios a subset of these is illustrated in Table 1. Based on the selected scenario, our model dynamically selects the appropriate column from the table representing the decadal sea levels. The model then linearly interpolates the sea level for single years between the start and end year of the corresponding decadal time interval. Land subsidence/uplift can also be incorporated in the calculations, as illustrated in Figure 3. The model allows the specification of the rate of land subsidence, to be applied to the entire county. Development of relative SLR estimates that incorporate both eustatic SLR and local land movement requires input of an estimate of local land subsidence on the inputs page. In the absence of a user specified value, the model defaults to an annual subsidence rate of 2.7 mm for all of New Jersey. 7 The land subsidence rate can be set to zero if the user chooses, and negative values can be used to represent uplift. The coastal property model iterates through each 150-m grid cell in the study area for each year of the selected period to identify those cells which are newly at risk of inundation in each successive year. Cells at risk of inundation are those which have an elevation less than the then-current sea level and for which there is a path for ocean water to access the cell. This approach assumes that water can inundate a particular cell only if one of its eight neighboring cells is either currently in open water status or considered abandoned that year or a prior year and therefore inundated under a previous time-step in the model (see Poulter & Halpin, 2008 for an analysis of water flow assumptions). Alternatively, users can

10 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 441 Figure 3. User interface. turn off the analysis of water flow and assume that a cell is at risk of inundation as long as it is located below the then-current sea level, regardless of whether a neighboring cell is inundated or a path for coastal water flow is available. Model Operation As shown in Figure 3, the user can change baseline unit costs and select from a variety of preprogrammed choices. The significance of these various features is discussed below. Geographic Parameters 1. Geographic Area: The geographic scope of any model run can be set to a county. Currently available choices are the four coastal counties in New Jersey. 2. Analysis Time Period: Userscanconstructthetimeperiodofanalysisbyselecting the start and end years of analysis. The maximum time period of the analysis can span between 2000 and Property Loss Method: Asdiscussedearlier,oneofthemeasuresofeconomic impact is the amount and value of land that may be affected by SLR in the future.

11 442 J. E. Neumann et al. Table 1 Sampling of pre-coded SLR trajectories MAGICC-Derived IPCC Scenarios Mid Ice Melt High Ice Melt Rahmstorf-Derived Scenarios SRES scenario B1 A1B Min B1 A1B A2 Max Sensitivity (degrees C) Data are for projected global sea-level change component and exclude land subsidence and uplift. 2. All values in cm above 1990 levels. 3. The IPCC Special Report on Emissions Scenarios (SRES) scenarios represent alternative storylines for global growth, energy use, and development, but do not include climate control initiatives. Each scenario has a complex description, but in general terms, the B1 scenario assumes adoption of clean and resource-efficient technologies, A2 assumes relatively high population growth scenario, and A1B assumes rapid economic growth with a balance of fossil and renewable energy use. 4. Climate sensitivity refers to the change in global mean air temperature that would result from a sustained doubling of the atmospheric CO 2 concentration. The model supports two methods of determining when a parcel of land can be deemed as lost to the rising seas. The Minimum Elevation value refers to the option where a grid cell is considered lost to the sea whenever any dry land within the grid cell becomes lower than sea level. The Centroid Elevation value refers to the option where a parcel is not considered lost until the grid cell centroid is below sea level. These two options provide a range of approaches to estimate the amount of land that could be at risk to SLR. Cost Parameters for Protection Strategies 1. Armoring Cost Parameters:Armoringcostsarisefromthefixedcapitalcostrelated to armoring structure installation, and subsequent annual maintenance expenditures stemming from maintenance of installed structures. The timing and location of structure installation is determined by when different parts of the shoreline are determined to be inundated. The model also enables the user to select differentiated maintenance cost rates depending on whether the structure is exposed to the open ocean or is located along more sheltered bay-front coastal areas. The default options for choosing annual maintenance cost rates along ocean and bay-front shores are

12 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 443 set to 10 percent and 4 percent, respectively, of the initial installation cost at that location. The model assumes that armoring will also be required along beaches when the sea has risen beyond a threshold level. Unless the user specifies an alternative, the model assumes that armoring is not required unless the sea level has risen by a foot. 2. Beach Nourishment Cost Parameters: Beachnourishmentcostsarerelatedtothe cost of sand and the amount of sand that will be needed. The total amount of sand that will be needed is also dependent on how frequently nourishment efforts are undertaken. The model assumes a default period of 10 years for repeating nourishment efforts along all beaches. Decreasing this time interval will increase nourishment cost estimates, whereas increasing this interval would decrease nourishment cost estimates. 3. Elevation Fill Cost Parameters: Elevationfillcostsaredeterminedbasedonthe idea that for all tax parcels that would need to be protected, there would be incurred a constant fixed cost (default $50,000) for raising the structure, and a variable cost depending on how much area would be inundated, and to what height dry lands on the parcel need to be raised. Because it is unreasonable to assume that large sized parcels would be elevated in their entirety, the model also allows the user to specify amaximumareathatmaybeelevatedforeveryparcel Discount Rate: The user can select a discount rate to bring all costs to the present day (2007 in the model). 5. Property Valuation:Themodelalsoallowstheusertospecifyhowpropertyvalues change over time. The three parameters that are needed to estimate the value of a property in a given year are the base year to which property values recorded in the database should be referenced, the estimated annual increase in per capita income, and the income elasticity. Sea-Level Rise Rate Sea-Level Rise Scenario: SLRscenariosweredescribedearlier.Weinterpretthe scenarios as cumulative eustatic estimates of SLR over time, with the effect of land subsidence and uplift over time also incorporated as indicated earlier. As a result, they incorporate baseline SLR, which may or may not be attributable to climate change or other causes. An incremental estimate of the impact of climate-induced SLR is best recovered by comparing two scenarios: one with climate-induced SLR, and a baseline rate. We do not provide default values for a baseline rate of SLR. Methodology for Estimating Protection Costs 1. Optimal Scenario Simulation: Todeterminetheeconomicallyefficientdecision regarding whether to protect or abandon a cell, the model first develops estimates of protection costs. These protection costs are estimated separately for open ocean sites versus cells within bays because the latter are exposed to less wave energy and likely require lower maintenance expenditures. For the purposes of determining ahomeowner sdecisionregardingtheoptimalapproach,weestimatethepresent value of maintenance over a 10-year period, plus capital cost. When estimating the total cost to society from protection over the full study area, however, we estimate the present value of maintenance from the date of installation through the end of the time period considered in the analysis. 2. State Land and Land-Use Protection Categories: Alternatively,themodelidentifies cells that are protected or abandoned based on both land-use and state plan

13 444 J. E. Neumann et al. (i.e., zoning) data. The model applies the user s selection of land categories to be protected and assumes all remaining categories would be abandoned. For cells where we estimate protection is warranted, the specific method of protection (e.g., armoring, nourishment, or elevation) is determined in the same way as the optimal scenario, but zoning determines the protect/abandon decision (that is, Q2 in Figure 2). Similarly, cost estimates are based on the same timing and unit cost assumptions. 3. Neighborhood Method: Asnotedearlier,themodelappliesaneighborhoodtestto determine the feasibility of group action. For each cell where the cost of protection is estimated to be lower than the value of the property (i.e., protection is economically efficient), the model examines the eight neighboring cells to consider the potential for action over multiple cells. We assume that the cell in question is ready for armoring as part of a neighborhood or group strategy if any of the neighboring cells were already deemed to be protected under previous time-steps of the model or they are at risk in the same year and all individually pass the protection test. Otherwise, if none of the neighbors pass the protection test or need to be protected, then the model assumes that the individual cell would be elevated rather than armored. This neighborhood test can be switched off if desired removing the neighborhood criterion implies that all properties that warrant protection will be armored or protected by beach nourishment, and the elevation response will never be triggered. Model Output After all user selections are made, a model run can be initiated. The model then uses the GIS information created and integrated into the Access database and user specified parameters to first calculate the inundation year for each parcel. Based on that information, the model then determines the total value of property that would be inundated before the end year as well as the quantity and corresponding cost of any elevation fill costs necessary to protect those areas. The results are first reported on a summary form that is generated automatically after the model has completed its simulation (Figure 4). The output form lists all the user choices in the left column, and summarizes costs of protection in the right column. The nature of these reported costs is described below. 1. Total Area and Value of Parcels at Risk:Thissectionreportsthetotalarea,aswell as the property values for the entirety of parcels at risk of inundation. These costs reflect the upper estimate of property values at threat to SLR for the given scenario, because often it will only be a portion of the parcel, and not the entire parcel that will be below sea level. 2. Area and Value of Parcels Abandoned: Thecostslistedunderthissectionprovide estimates for only those parts of parcels that would actually be lost to SLR in the absence of active protection efforts. The model calculates the per unit area estimate of the value of land and structure for every parcel, and then uses these values to estimate the land, structure and total property values of that part of the parcel that would actually be lost to the sea. 3. Armoring Costs:Thissectionreportsthelengthandcostsassociatedwitharmoring for back bay and open ocean shorelines separately. 4. Beach Nourishment Costs: Thissectionreportsthetotallength,area,andcostsof sand nourishment along beaches.

14 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 445 Figure 4. Sample model output. 5. Elevation Fill Costs: Thissectionreportstheactualareathatwouldneedtobe raised, and the total costs associated with the Elevation Fill protection strategy in non-beach areas. 6. Total Costs: The total costs due to loss of property and costs of armoring, nourishment, and elevation fill are added up to report the total costs. The model is also capable of producing cumulative discounted damage costs over the full modeling period, as shown in Figure 5, and maps displaying response results by grid cell, as shown in Figure 6. 9 Results Model results for four of the ocean-facing New Jersey counties in the study area (Figure 7) are shown in Table 2, for three SLR scenarios. These results reflect default values for all parameters, as shown in Figure 3, including use of the optimal response mode, the minimum elevation option for assessing cells vulnerable to SLR, and a 3 percent discount rate. The three SLR scenarios chosen for this results summary span the range of alternatives available in the model. The low scenario is consistent with just over 28 cm SLR by 2100, the middle scenario more than twice that, almost 67 cm by 2100, and high scenario more than 126 cm by As expected, all measures of impact increase with more SLR, but the overall increase in virtually all measures of damage is sublinear in relation to SLR. The change in acres at risk varies by county, consistent with elevation for example, in Monmouth County vulnerable land increases by about 10 percent for each rough doubling of SLR by

15 446 J. E. Neumann et al. Figure 5. Illustrative cost/damage trajectory. 2100, but in Cape May County vulnerable land increases by about 25 percent between the low and middle scenarios, and about another 1/3 between the middle and high scenarios. Differences between counties are attributable to elevation, but the relatively high quantities of vulnerable land in the low scenario in all counties is due to the minimum elevation Figure 6. Sample map output illustrating response results.

16 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 447 Figure 7. New Jersey coastal study area. assumption use of the grid cell centroid approach in the vulnerability assessment can dramatically reduce the acres at risk. For example, use of the grid cell centroid assumption in Cape Map County, for the low scenario, reduces acres at risk to about 12,500, a 45 percent reduction, although total damages are reduced by only about 25 percent. We would expect additional variations in the acres at risk once fine-resolution LIDAR data are available throughout the study area and can be applied in this model. The optimal response, and its cumulative cost, varies substantially by county. Some of the variation is due to topography; for example in Monmouth County the costs of response per acre at risk are relatively modest compared to other counties, because the relatively straight coast implies that protection of the front line of vulnerable cells also protects a large land area behind this front line. Costs in Ocean County are substantially higher than in Cape May, even though Cape May has more low-lying area, primarily because Cape May includes a substantial area along Delaware Bay with lower property value, much of which is forecast to be abandoned. The variability in response and costs reinforces the value of

17 448 J. E. Neumann et al. acareful,site-specific,andcomprehensivemodelindevelopingaccurateestimatesofthe full cost of SLR. The effect of discounting is readily apparent, and is another reason why damages are sublinear with respect to SLR scenario, as responses that occur later in the modeling period are discounted relative to those that occur closer to the present. Although insights can be gained by examining undiscounted results, the discounted results are probably most relevant both for policy purposes and local adaptation decisions. In policy decisions, damages are roughly equivalent to the benefits of mitigating SLR, and should appropriately be compared to costs that have different time profiles, making the discount rate a critical assumption. In adaptation decisions, the discount rate is consistent with the option for local governments to finance adaptive measures over time. The substantial effect of discounting also reinforces the importance of dynamic modeling, highlighting a major advantage of our approach which is capable of estimating the timing of SLR response through the century. It is interesting to note that the need for beach nourishment is not altered across scenarios areas amenable to nourishment but vulnerable to SLR are expected to be nourished with even modest SLR. The total need for sand, however, and hence the cost of nourishment, increases roughly linearly with higher SLR scenarios. The undiscounted stream of nourishment costs is actually supralinear in relation to SLR, because of the recurring need for increasing amounts of sand to effectively respond to contemporaneous SLR. The results in Table 2 reflect an assumed 10 year renourishment period in all counties. The elevation response, adopted less frequently than the other three responses, appears to be more cost-effective than armoring as a protective strategy but more expensive, on average, than abandonment. As a result, elevation is likely to be a good intermediate option for responding to SLR in areas with less-concentrated development and intermediate property values. It is important to remember, however, that in many areas the elevation response we model may not completely restore an area and its full coastal amenities to baseline, pre-slr levels. Raising structures and a portion of the land around them probably maintains many human uses of coastal land, but some features, such as beaches, would be lost to erosion, and others, such as utilities and road access, might require additional costs to maintain. Detailed examination of the response maps (such as that provided in Figure 6) suggest that most areas where an elevation response is optimal are located along back bay shores not characterized by beach topography, but where road access can be difficult to maintain. Further work is needed to estimate whether residual damages of this type might result from implementing the elevation option. The results in Table 2 reflect the optimal response mode of the model, but the model is also capable of modeling response based on zoning designations or land-use characteristics. In some cases, the effect can be substantial, as illustrated in Figure 8. One of the most notable differences between results in the two panels of Figure 8 is a shift away from hard structure and beach nourishment protection in ecologically sensitive areas, such as barrier islands and some back bay marshes. Note that, in the right panel of Figure 8, the entire shore of Long Beach Island, designated as ecologically sensitive in New Jersey s state plan, is modeled as abandoned, with inland areas of the island modeled as nourished, a dramatic difference from the left panel of Figure 8. The result here is determined by the zoning, so from a modeling perspective it is not surprising, but it highlights that different outcomes may result from purely private considerations (the optimal model result) versus more explicit consideration of ecological value. Clearly, consideration of the non-market value of ecological resources in SLR response modeling is an important area for further

18 Table 2 Model results for study area (All estimates are cumulative through 2100) Monmouth Ocean Atlantic Cape May Low SLR Scenario (MAGICC: Mid Ice Melt, B1, 2 C) Acres at Risk (acres) 13,005 18,876 16,496 22,662 Value of at Risk Property ($) $6,260,000,000 $86,800,000,000 $17,700,000,000 $32,900,000,000 Acres Inundated (acres) 33 2,446 1,129 6,082 Value of Abandoned Property ($) $676,000 $15,400,000 $17,200,000 $29,300,000 Back Bay Armoring Length (miles) Open Ocean Armoring Length (miles) Total Armoring Cost ($) $38,400,000 $279,000,000 $246,000,000 $259,000,000 Nourishment Length (miles) Total Nourishment Cost ($) $69,000,000 $304,000,000 $129,000,000 $158,000,000 Elevation Fill Area (acres) Total Elevation Fill Cost ($) $1,930,000 $34,800,000 $21,800,000 $18,800,000 Total Costs ($) $110,000,000 $633,000,000 $414,000,000 $465,000,000 Middle SLR Scenario (MAGICC: High Ice Melt, A1B, 4.5 C) Acres at Risk (acres) 14,233 22,690 19,454 28,728 Value of at Risk Property ($) $6,290,000,000 $88,300,000,000 $18,500,000,000 $35,600,000,000 Acres Inundated (acres) 111 3,414 1,829 8,184 Value of Abandoned Property ($) $1,840,000 $22,200,000 $27,100,000 $43,700,000 Back Bay Armoring Length (miles) Open Ocean Armoring Length (miles) Total Armoring Cost ($) $66,600,000 $487,000,000 $372,000,000 $424,000,000 Nourishment Length (miles) (Continued on next page) 449

19 Table 2 Model results for study area (All estimates are cumulative through 2100) (Continued) Monmouth Ocean Atlantic Cape May Total Nourishment Cost ($) $103,000,000 $455,000,000 $193,000,000 $237,000,000 Elevation Fill Area (acres) Total Elevation Fill Cost ($) $2,360,000 $46,600,000 $26,700,000 $25,700,000 Total Costs ($) $174,000,000 $1,010,000,000 $619,000,000 $730,000,000 High SLR Scenario (Rahmstorf: Max) Acres at Risk (acres) 16,257 28,127 23,624 37,151 Value of at Risk Property ($) $6,310,000,000 $89,700,000,000 $19,200,000,000 $37,900,000,000 Acres Inundated (acres) 128 4,676 2,685 12,315 Value of Abandoned Property ($) $2,500,000 $28,100,000 $36,200,000 $61,000,000 Back Bay Armoring Length (miles) Open Ocean Armoring Length (miles) Total Armoring Cost ($) $80,700,000 $628,000,000 $456,000,000 $541,000,000 Nourishment Length (miles) Total Nourishment Cost ($) $139,000,000 $613,000,000 $260,000,000 $318,000,000 Elevation Fill Area (acres) Total Elevation Fill Cost ($) $2,960,000 $54,000,000 $29,800,000 $32,100,000 Total Costs ($) $225,000,000 $1,320,000,000 $782,000,000 $953,000,

20 AGIS-BasedFrameworktoAssessImpactsofSea-LevelRise 451 Figure 8. Effect on response results of optimal (left panel) versus land-use-based (right panel) response estimation. (Figure is provided in color online.) research. While it may be unlikely that private landowners recognize the full benefit of ecological service flows, and therefore these landowners would have a strong incentive to pursue hard structure and beach nourishment protection for their threatened properties, from a broader social perspective this private incentive could lead to substantial ecological damages. Discussion Compared to prior efforts, our new model of the impacts of SLR enjoys substantial advantages. As noted earlier, the comprehensive areal coverage of the model made possible by our GIS-based approach means that a far greater range of site-specific conditions are reflected in the results. In addition, the parsimonious nature of the model allows a countylevel run to be completed in well under 60 seconds, making it possible to generate multiple runs in a short span of time and allowing detailed exploration of uncertainties in SLR rate, economic parameters, and response modeling. Although for a time it appeared that estimates of SLR were converging on SLR projections of approximately 35 to 65 cm by 2100, recent research highlighting the large uncertainties in predicting when dynamic ice sheet melting in Greenland and West Antarctica might occur (Rahmstorf, 2007; Pfeffer et al., 2008), coupled with uncertainties in the potential for substantial regional anomalies in eustatic SLR (Yin et al., 2009), suggest that the flexibility of this model could be critically important in adaptation planning and decision-making. Another key advance made possible by the GIS-based approach is the ability to map results. This capability allows not only for visualization of alternative futures but also for overlays of the modeling results on land-use maps, as a consistency and reasonableness check. In addition, the mapping function facilitates other forms of spatial analysis two