Systems Approaches for Coastal Hazard Assessment and Resilience
Summary and Keywords
The framework presented herein supports a changing paradigm in the approaches used by coastal researchers, engineers, and social scientists to model the impacts of climate change and sea level rise (SLR) in particular along low-gradient coastal landscapes. Use of a System of Systems (SoS) approach to the coastal dynamics of SLR is encouraged to capture the nonlinear feedbacks and dynamic responses of the bio-geo-physical coastal environment to SLR, while assessing the social, economic, and ecologic impacts. The SoS approach divides the coastal environment into smaller subsystems such as morphology, ecology, and hydrodynamics. Integrated models are used to assess the dynamic responses of subsystems to SLR; these models account for complex interactions and feedbacks among individual systems, which provides a more comprehensive evaluation of the future of the coastal system as a whole. Results from the integrated models can be used to inform economic services valuations, in which economic activity is connected back to bio-geo-physical changes in the environment due to SLR by identifying changes in the coastal subsystems, linking them to the understanding of the economic system and assessing the direct and indirect impacts to the economy. These assessments can be translated from scientific data to application through various stakeholder engagement mechanisms, which provide useful feedback for accountability as well as benchmarks and diagnostic insights for future planning. This allows regional and local coastal managers to create more comprehensive policies to reduce the risks associated with future SLR and enhance coastal resilience.
Worldwide, low-lying coastal land margins are becoming increasingly vulnerable to natural and manmade disasters due to the effects of climate and socioeconomic change (Barbier, 2014; IPCC, 2014). It is estimated that 1.9 billion people (27% of the world’s population) live within 100 km of a shoreline and at an elevation lower than 100 m (Kummu et al., 2016). In the United States, population density in coastal counties far exceeds that of the nation as a whole (446 and 105 persons/square mile, respectively), and this trend is predicted to continue (NOAA, 2013). In addition to human communities, the coastal land margin includes ecologically and economically significant estuaries and wetlands that provide food, shelter, and nursery areas for commercially harvested fish and shellfish, and protect coastal communities by mitigating the impacts of storm surge and erosion (Barbier et al., 2011; NOAA, 2011). In 2011, 45% of the U.S. gross domestic product was produced in shoreline counties (NOAA, 2012). Global climate change and sea level rise (SLR) in particular have the potential to affect these coastal environments with more extensive tidal and storm surge inundation, increased erosion, and wetland loss. Low-gradient coastal landscapes are especially vulnerable to the effects of SLR, which could be detrimental for human communities and estuarine habitats. As populations increase, coastal areas will be susceptible to additional stresses associated with land-use and hydrologic changes (Wong et al., 2014). With more human lives at stake, along with economic output, measures to enhance coastal community resiliency are no longer an option, but a necessity.
Understanding the future of coastal environments depends on scientific evaluations of the impacts of climate change. Observations and modeling of individual coastal systems are insufficient for scientifically defensible, detailed, and credible assessments of the response of the coastal region under future conditions. Rather, synergetic studies that integrate dynamic interactions and responses among physical, ecological, and anthropogenic environments are needed for more holistic evaluations (Passeri et al., 2015c). The purpose of this chapter is to propose and encourage the use of a System of Systems (SoS) approach to assess potential ecologic and economic impacts of future coastal hazards under global climate change, and SLR in particular, herein coined “the coastal dynamics of SLR.” This framework considers the integrated responses and feedbacks of multiple interdependent coastal systems to project how coastal environments will evolve under future SLR. Unique benefits of using a SoS approach include the capability to model the bio-geo-physical system, link that modeling to the historic record, and produce a dynamic response to SLR. Incorporating economic data and ecosystem services valuations into the SoS permits stakeholder groups to better understand and evaluate future coastal hazards and enhance human community coastal resiliency. Ultimately, a SoS approach supports the translation of data from science to application with transdisciplinary research results that enable coastal managers to make more informed decisions to manage human and natural communities in the future.
What Is a System of Systems?
According to Maier (1998), in the context of systems engineering, a system can be considered a SoS when:
(1) its components fulfill valid purposes in their own right and continue to operate to fulfill those purposes if disassembled from the overall system, and (2) the components systems are managed (at least in part) for their own purposes rather than the purposes of the whole (p. 268).
While the individual constituents of a SoS can differ and function autonomously, new properties and patterns may emerge from their interconnectivity. These properties/patterns cannot be realized a priori and cannot be inferred from the systems’ individual constituents or their individual properties alone (Keating, 2008). In the context of coastal hazards, a SoS approach allows nonlinear and dynamic effects of SLR to emerge among individual coastal systems that would otherwise be unobserved using simplified models. In doing so, more complex interactions and feedbacks among individual systems are accounted for, which provides a more holistic understanding of how the larger system may evolve over time.
The SoS for the coastal dynamics of SLR contains smaller subsystems such as hydrodynamics, morphology, ecology, and land use that are tied to political and social system implications (Figure 1). Each of these subsystems contains components that can reach higher levels of complexity and refinement. For example, hydrodynamics are comprised of tides, waves, and storm surge. Likewise, coastal morphology includes features such as barrier islands, beaches, and dunes. Each subsystem interacts dynamically on different spatial, temporal, and energy scales. To capture the function, interactions, and dynamic responses of subsystems to drivers such as global climate change, integrated models are needed that link SLR to carbon emission scenarios so that the full climate change impact is considered. This scenario-based approach produces projections of potential dynamic responses rather than deterministic predictions. Modeled projections can then inform economic impact assessments and ecosystem services valuations, which can be used with projections of how the system will evolve to produce management tools though stakeholder engagement. These tools, along with judgement, allow coastal managers to make more informed decisions on coastal hazards, which can be fed back into the suite of integrated models for evaluation of the impacts of potential management actions. This distinguishes the SoS approach from existing methods to assess coastal hazards such as the Source–Pathway–Receptor–Consequences (Gouldby and Samuels, 2005) approach, in which a hazard (e.g., SLR) is connected linearly to a risk (e.g., loss of ecosystems), but integrated responses and feedbacks of other risks (e.g., loss of shorelines), and the effects of coastal management decision making are not accounted for. The following sections detail the various subsystems and components that comprise the SoS approach to the coastal dynamics of SLR.
Sea Level Rise
The first component to consider in the SoS for the coastal dynamics of SLR is global climate change and SLR in particular, which drives dynamic changes in each subsystem. The main factors contributing to global mean SLR include thermal expansion of sea waters, land ice loss, and changes in land water storage. Spatially, SLR is nonuniform, primarily as a result of changes in ocean density structure caused by temperature and salinity variations (i.e., steric effects) (Bindoff et al., 2007), as well as effects related to the response of the Earth to present-day land ice melt and the last deglaciation. Additionally, localized vertical land movement such as subsidence or uplift (due to natural causes or human activities) can amplify or reduce the effects of SLR. These three components can cause large deviations in local relative sea level rise (RSLR) measurements compared to global SLR observations (Cazenave & Le Cozannet, 2013). Historic tide gauge records indicate a global mean SLR between 1.6 mm/year and 1.8 mm/year over the 20th century (Church & White, 2006; Jevrejeva, Grinsted, Moore, & Holgate, 2006; Jevrejeva, Moore, Grinsted, & Woodworth, 2008; Church & White, 2011). According to high-precision satellite altimetry data, global mean SLR has accelerated with global rates as high as 3.4 mm/yr between 1993 and 2009 (Nerem, Chambers, Choe, & Mitchum, 2010; Hay, Morrow, Kopp, & Mitrovica, 2015). Climate change modeling indicates that sea levels will continue to rise in the upcoming decades and even centuries (IPCC, 2013; Levermann et al., 2013). Therefore, projecting future global mean sea level is critical for assessing vulnerabilities to built and natural environments. Projecting future rates of SLR can be challenging due to the complexities in contributory processes such as glacier melt, air temperature, CO2 levels, thermal expansion, and volcanic, solar, and greenhouse gases (Church & White, 2006; Jevrejeva et al., 2006). Although historic rates of SLR can be used to assess future impacts, they cannot account for predicted changes and associated uncertainty in future SLR. Projections of future global mean SLR can be used to consider multiple future conditions and develop response options, given the range of uncertainty (Parris et al., 2012). For example, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report projects that global mean sea level will rise between 52 and 98 cm by the year 2100 under the highest emissions scenario, and 28–61 cm under the lowest emissions scenario (Church et al., 2013). Similarly, Parris et al. (2012) identified four scenarios of global mean SLR by 2100: the lowest (0.2 m) is derived from a linear extrapolation of historical mean sea level using tide gauge records dating back to 1900; the intermediate-low (0.5 m) is determined using the upper end of the IPCC Fourth Assessment Report (AR4) global SLR projections from climate models employing the B1 emissions scenario; the intermediate-high (1.2 m) is derived from the average of the high end of semiempirical global SLR projections that use statistical relationships between observed global sea level change, including recent ice sheet loss and air temperature; and the highest (2.0 m) projection is determined using the IPCC AR4 global SLR projection with estimates of maximum possible glacier and ice sheet loss by the end of the century.
Coastal Dynamics of Sea Level Rise
In this section, three dynamic subsystems—namely coastal morphology, ecology, and hydrodynamics—are selected to illustrate how individual subsystems integrate to form the larger coastal system. Although each subsystem responds to the effects of SLR on different spatial, temporal, and energy scales, the responses are dynamic and interrelated.
Coasts are dynamic systems that continuously transform as a result of integrated geomorphic and oceanographic processes that occur over different temporal and spatial scales (Cowell et al., 2003a, 2003b). The impacts of short-term processes (such as storms) on barrier islands and beaches are regulated largely by dune elevations (Sallenger, 2000; Stockdon, Sallenger, Holman, & Howd, 2007; Long, de Bakker, & Plant, 2014), and can result in erosion, overwash, or breaching, depending on the island’s characteristics (Sallenger, 2000; Plant & Stockdon, 2012). Over longer timescales (decades to centuries), the combination of storms and SLR will evolve barrier island morphology (Fitzgerald, Fenster, Argow, & Buynevich, 2008; Gutierrez, Williams, & Thieler, 2009; Wahl & Plant, 2015). This can produce rapid shoreline erosion as a function of high rates of relative SLR (Morton, Miller, & Moore, 2004) and storm impacts (Fearnley, Miner, Kulp, Bohling, & Penland, 2009; Stockdon et al., 2012). Additionally, the local sediment budget and sediment texture affects long-term barrier island evolution; barrier islands with limited or finer sediment will erode more quickly than barriers with abundant or coarse sediment (Twichell, Pendleton, Baldwin, & Flocks, 2009; Twichell, Flocks, Pendleton, & Baldwin, 2013).
The most straightforward approach to assess the long-term shoreline response to SLR is to consider inundation under a static, or “bathtub” approach based on the elevation of the land and the amount of SLR. Coasts with mild slopes will experience more inundation for a given SLR than coasts with steep slopes (Zhang et al., 2004). This approach can be problematic due to large uncertainties in the topographic data (Gesch, 2009; Gesch, Gutierrez, & Gill, 2009) as well as neglecting hydrodynamic processes that may cause shoreline erosion (Passeri et al., 2015c). While numerical models are capable of predicting barrier island response to specific storm events with high skill (Roelvink et al., 2009; McCall et al., 2010), they are often too complex and inefficient to make long-term projections in response to multiple storms and SLR. Instead, simplified morphodynamic models based on empirical rules and analyses (Cowell, Roy, & Jones, 1995; Storms, Weltje, van Dijke, Geel, & Kroonenberg, 2002; Stolper, List, & Thieler, 2005; Lorenzo-Trueba & Ashton, 2014), as well as statistical approaches (Hapke & Plant, 2010; Gutierrez, Plant, & Thieler, 2011; Yates & Le Cozannet, 2012; Gutierrez, Plant, Thieler, & Turecek, 2015; Plant, Thieler, & Passeri, 2016) can be more effective for examining long-term barrier island evolution. These models rely on capturing joint correlations between morphological parameters and their drivers, as well as feedback mechanisms (Plant et al., 2016). Probabilistic models have indicated that as SLR continues to accelerate along low-gradient coastlines, long-term shoreline erosion rates will increase (Gutierrez et al., 2011; Plant et al., 2016), while dune elevations will decrease as a result of frequent overwash or rapid evolution in which a low coastal elevation is maintained (Plant et al., 2016).
Another subsystem to consider in the coastal dynamics of SLR is ecology. Ecosystems are unique compared to the other coastal subsystems because they are self-organized, adaptive, and contain many feedback mechanisms (Jorgensen & Fath, 2011). For example, coastal salt marshes that are found at the coastal land margin sequester carbon, improve water quality, provide habitats for commercially harvested species, and dampen wave energy, thereby protecting nearby shorelines from erosion during storm events (Moller, Spencer, French, Leggett, & Dixon, 1999; NOAA, 2011; Ouyang & Lee, 2014). These ecological subsystems are governed by their own physical processes and influenced by changes in other subsystem’s physical processes (Silliman & Bertness, 2002). Salt marsh growth is governed by elevation, tidal inundation, hydroperiod, sediment supply, and biological dynamics (Morris, Sundareshwar, Nietch, Kjerfve, & Cahoon, 2002; Stralberg et al., 2011). The elevation of a marsh platform can increase through particle capture during tidal inundation (Mudd, D’Alpaos, & Morris, 2010). Proximity to tidal creeks is crucial for deposition; areas near the banks of the creeks tend to accrete faster than the inner marsh (Townend, Fletcher, Knappen, & Rossington, 2011). If currents in tidal creeks are flood-dominant, there is likely a higher suspended sediment concentration at the marsh boundary, which supplies more marine sediment to the marsh. If currents are ebb-dominant, sediment supplies are likely reduced as sediment is transported seaward (Friedrichs & Perry, 2001). Hydroperiod (i.e., the period of time a marsh is inundated), which is dependent on the local tidal range, dictates sediment transport across the marsh surface (Reed, 1990). An increase in hydroperiod allows for inorganic sediment accretion until the hydroperiod eventually decreases; at this point, stress on the vegetation decreases accretion, once again increasing the hydroperiod (Friedrichs & Perry, 2001). Additional factors contributing to platform accretion include deposition of organic matter from root growth (Mudd et al., 2010) and inorganic matter from mineral sediment (Morris et al., 2016) and nearby eroded shorelines, especially during storm events (Phillips, 1986; Cahoon et al., 1995; McKee & Cherry, 2009; Baustian & Mendelssohn, 2015).
The spatial extent of salt marshes in the future relies on their ability to grow vertically at rates greater than relative SLR or to migrate inland before their seaward boundary is eroded (Kirwan, Temmerman, Skeehan, Guntenspergen, & Fagherazzi, 2016). The marsh platform elevation must continuously adapt through sedimentation towards an equilibrium that keeps pace with changes in mean sea level (Morris et al., 2002). A small increase in sea level relative to the platform elevation can increase accretion and therefore productivity (Reed, 1990; Nyman, Walters, Delaune, & Patrick, 2006; Alizad et al., 2016). Relatively low rates of SLR can also reduce depths on adjacent tidal flats, which increases wave dissipation and sediment deposition, and allows the marsh boundary to prograde (Mariotti & Fagherazzi, 2010). However, if the rate of SLR is too high and exceeds the rate of accretion, the marsh will drown (Reed, 1990; Mariotti & Fagherazzi, 2010; Alizad et al., 2016; Sampath & Boski, 2016). Salt marshes may also attempt to migrate landward under SLR, which may be constrained by anthropogenic infrastructure such as roads or buildings (Friedrichs & Perry, 2001; Alizad et al., 2016; Sampath & Boski, 2016). Studying the integrated processes of salt marshes requires the coupling of dynamic models that capture biophysical feedback mechanisms (Reed, 1990). However, developing models that combine realistic subsystem marsh processes with the dynamics of larger-scale system processes can be challenging (Stralberg et al., 2011). Models that neglect bio-geo-physical feedback processes or don’t account for marsh migration tend to underestimate resilience (Kirwan et al., 2016).
The last subsystem presented is hydrodynamics, namely astronomic tides and storm surge (tropical or extratropical), which is the backbone of the SoS approach to the coastal dynamics of SLR. Hydrodynamic models governed by forms of the Navier–Stokes equations provide an opportunity to examine the nonlinear feedback mechanisms of coastal flooding and allow for more accurate assessments of tides and storm surge inundation under SLR than static (i.e., “bathtub”) models (Atkinson, Smith, & Bender, 2013; Bilskie, Hagen, Medeiros, & Passeri, 2014; Bilskie et al., 2016a). Static models typically assume a spatially constant and linear increase in flood depths and do not account for physics-based methods to inundate new land. In addition, static models are not always straightforward and can, counterintuitively, be complicated to apply (Hagen & Bacopoulos, 2012; Zhang, Li, Liu, Xu, & Shen, 2013). This is especially true along low-gradient coastal landscapes where minor increases in SLR and flood depths result in vast areas of new inundation (Passeri et al., 2015c).
Astronomic tidal amplitudes and tidal velocities have been shown to increase under SLR. As mean sea level increases, tidal inlets grow and enhance tidal flow volume through inlet–bay systems. Additionally, changes to tidal velocities may contribute to further erosion or deposition of sediments as tidal regimes can become more ebb or flood dominant. These changes have potential to alter salt marsh and seagrass productivity and oyster recruitment through availability of sediment and nutrients within the water column (Passeri, Hagen, Medeiros, & Bilskie, 2015b; Passeri et al., 2015c, 2016). SLR is also projected to modify flooding patterns during tropical cyclone events and can increase peak surge. Many regions along low-gradient coasts such as the northern Gulf of Mexico are susceptible to larger peak surges (up to 1.0 m higher than the rise in SLR), earlier arrival of the peak surge, lengthier inundation times, and additional inundated land (Bilskie et al., 2016a).
The SoS approach to the coastal dynamics of SLR advocates the use of integrated dynamic models to capture subsystem responses and feedback mechanisms to provide more comprehensive evaluations of how the coastal system may evolve under different scenarios. A physics-based numerical hydrodynamic model provides the means to simulate the coastal dynamics of SLR as governed by shallow water equations (Bilskie et al., 2016b). Although hydrodynamics can be computed stand-alone, it is imperative that the various subsystems are linked to hydrodynamics (and vice versa) using a SoS approach. Information about future morphology, such as shoreline and barrier island configuration and dune elevations can be implemented into a hydrodynamic model to represent potential coastal landscape changes and their effect on hydrodynamics. For example, future oceanic shoreline recession and eroded dunes can modify the path and pattern, and increase magnitude of tide and storm-induced inundation, especially along barrier islands (Passeri, Hagen, Bilskie, & Medeiros, 2015a; Passeri et al., 2015b; Bilskie et al., 2016a; Passeri et al., 2016). This can change the depth of the water table, inducing saltwater intrusion into groundwater, which can negatively affect interdependent ecosystems including marshes, shrub thickets, and maritime forests (Masterson et al., 2013). Additionally, coastal landscape changes can reduce dune vegetation growth (Feagin, Sherman, & Grant, 2005) and alter habitats for nesting species such as sea turtles (Reece et al., 2013), shore birds (Gieder et al., 2014), and beach mice (Chen, Wang, Wang, Tawes, & Rollman, 2014).
Changes in land use can be incorporated into the hydrodynamic model to represent future land roughness (Bilskie et al., 2016a). For example, urbanization results in newly developed areas, which may reduce the area of coastal forests, thereby altering the propagation of overland inundation (i.e., there is less resistance and wind momentum is more directly transferred as water flows across a paved surface than through a dense forest). Additionally, projections of salt marsh evolution can be incorporated into the hydrodynamic model. Salt marshes attenuate surge and dissipate wave energy through their vast expanse and relatively high bottom friction (Smith, Cialone, Wamsley, & McAlpin, 2010; Moller et al., 2014), which can have a substantial impact in terms of tide and tropical cyclone-induced overland inundation (Bilskie et al., 2014, 2016a; Passeri et al., 2016).
The SoS approach requires the use of high-resolution models to describe small-scale processes, which limits the spatial scale at which it can be applied; however, this allows decision-making to occur on a local scale to address the needs of individual communities and implement specific strategies to mitigate future coastal hazards. Overall, the SoS approach permits more realistic scientifically-based assessments that can better guide stakeholders, coastal managers, and decision makers to prepare, mitigate and adapt coastal communities and ecosystems to a changing landscape and future SLR (Tribbia & Moser, 2008; Passeri et al., 2015c).
Economic and Ecosystem Valuations
As the coast evolves, natural resource management plans will strive to achieve long-term sustainability of ecological habitats. However, these efforts may conflict with future land-use decisions since anthropogenic development tends to be favored along stable areas of barrier islands (Masterson et al., 2013). Output from the integrated coastal subsystem models is used to quantify economic and ecologic impacts through valuation assessments. The results of both types of valuations—economic impact and ecosystem services—whether measured in monetary or nonmonetary terms, are critical for resource management and adaptation strategies.
Recognition of the economic importance of the environment and impact on human well-being beyond traditional measures (e.g., income, jobs, taxes) continues to grow as federal and state agencies look for opportunities to make the case for natural resource protection and enhancement (Yoskowitz & Russell, 2015). Ecosystem services assessment and valuation has increased in use to connote importance of natural resources, especially in coastal and marine environments (Barbier, 2012; Yoskowitz et al., 2016). Applying ecosystem service assessments to benefits specifically derived by Natural and Nature-Based Features (NNBFs), or coastal green infrastructure (e.g., living shorelines), has been lacking (OSTP, 2015). Yet the need is great, given damage that tropical storms and persistent nuisance flooding can inflict and the role that NNBFs can play in reducing impacts. For example, the estimated impact of hurricanes Sandy and Katrina was $67 billion and $151 billion, respectively (NOAA, 2015). Additionally, studies have calculated the traditional economic impact of storm surge and the benefits that enhanced “natural” protection would provide (Costanza et al., 2008; Barbier, Georgiou, Enchelmeyer, & Reed, 2013) but have failed to connect it specifically to SLR scenarios and NNBF enhancement. In a meta-analysis, Geden, Kirwan, Wolanski, Barbier, and Silliman (2011) stress the need to close the gaps on “… the mechanistic and context-dependent aspects of shoreline protection” (p. 8) and connecting to the economic impact of surge and nuisance flooding.
U.S. Federal policy regarding valuation of ecosystem services and coastal green infrastructure has undergone major advancements during 2015. The White House recently issued two important directives. The first is the Ecosystem-Service Assessment: Research Needs for Coastal Green Infrastructure (OSTP, 2015), which identifies significant opportunities for advancing use of NNBFs as well as filling science gaps, including explicit linkages between biophysical structure, function, and processes and the value of ecosystem services. The second was a directional memo (Incorporating Ecosystem Services into Federal Decision Making, p. 1) in October of 2015, which “… provides direction to agencies on incorporating ecosystem services into Federal planning and decision making.” These new policies provide viable avenues to initiate connecting NNBFs with ecosystem services and examining impacts on community resilience.
Ecosystem service assessment should be highly interdisciplinary, involving ecologists, physical scientists, modelers, economists, and social scientists, and ideally, transdisciplinary with the input from nonacademic stakeholders. To be successful, extensive research and data in these disciplines as well as input from communities of stakeholders are required (Yoskowitz & Russell, 2015). Looking at the societal impact of SLR through an “ecosystem services lens” is limited. The biophysical science, a necessary input for an ecosystem services assessment, is relatively well developed (Morris et al., 2002; Akumu, Pathirana, Baban, & Bucher, 2011; Hagen, Morris, Bacopoulis, & Weishampel, 2013) but the connection to human well-being is lacking (NRC, 2012) along with the consideration of NNBF (coastal green infrastructure) (OSTP, 2015).
Economic impact and ecosystem valuations fit into the SoS for the coastal dynamics of SLR first through the explicit recognition that humans and their activities are part of the natural environment and not separated from it, and that “environmental” problems are created, and hopefully solved, by humans. For example, nuisance flooding increases in Houston, Texas as a result of SLR, cutting off roads to a factory that cannot ship its product out on those days. Two things can happen. One, the factory adapts and raises the road. This has associated impact on jobs, income, and taxes. Or two, the factory does not adapt and sales are diminished and there are once again associated impacts. Assessment of “traditional” economic impact from commercial and noncommercial activities can be measured in a relatively straightforward manner using economic input–output models. Economic impact analysis—based upon input–output models—traces spending by a program or policy through the economy. The cumulative effects are measured monetarily. Results show direct effects, secondary effects including indirect and induced effects, and total output effects. Direct effects show the initial change in the economic activity associated with the final demand value, or how the industry is experiencing the change. Indirect effects include all the other supply chain responses and local employment indirectly affected by backward linkages. Induced effects refer more to household spending and are the result of spending of wages by the workers responsible for the direct and indirect effects. Indirect and induced effects are commonly referred to as secondary effects or spillover effects (IMPLAN Group, 2013). Here the economic activity must be connected back to biophysical changes in the environment due to SLR. Therefore, the understanding of the economic system (traditional) is used to identify changes in the biophysical system and assess the impact to the economy.
Valuing ecosystem services—the benefits that we enjoy from our natural environment—that aren’t captured in market transactions can be challenging. These services can range from carbon sequestration to recreational fishing opportunities and aesthetic values (Farber et al., 2006). All of these services could be impacted by SLR as it changes the biophysical makeup of a system if we were to look at the provisioning of these services emanating from a particular biophysical feature (marsh, oyster reef, and seagrass). This is traditionally how ecosystem service assessments are conducted (NRC, 2012). However, the services should be thought of as being supplied by a “system.” For example, if one were to only look at the connection between SLR, marsh, and recreational fishing and conclude that recreational fishing would be negatively impacted, this could be incorrect in systems that have multiple biophysical features that recreational species have fidelity to (mangrove, seagrass, and oyster reefs). As areal extent of marsh diminishes other habitats may expand their range.
Translating Science to Application for Societal and Coastal Benefits
There is significant concern and discussion among practitioners, scholars, politicians, and the public about the gap between what scientists and decision makers consider to be useful climate change-related knowledge (Lemos, Kirchoff, & Ramprasad, 2012). It has been argued that this complex and pressing issue calls for research approaches that are “transdisciplinary” (Allen, Kruger, Leung, & Stephens, 2013). According to the emerging body of literature on this subject, the key distinctive characteristics of transdisciplinary research are that it focuses on a real-world problem, has an evolving and iterative methodology that integrates different scholarly fields, and actively engages nonacademic stakeholders throughout the project duration (Wickson, Carew, & Russell, 2006; Levy, 2011; Allen et al., 2013). The term “stakeholder” has been defined in many ways but it is generally used to describe groups or individuals with a vested interest (e.g., monetary, professional, personal) in a particular research project or who will be impacted by a decision outcome (McNie, 2007; Phillipson, Lowe, Proctor, & Ruto, 2012; NOAA Office for Coastal Management, 2015). Identifying, analyzing, and understanding the stakeholders in a particular issue is vital and there are various approaches (Meffe, Nielsen, Knight, & Schenborn, 2002; Carney, Whitmarsh, Nicholson-Cole, & Shackley, 2009; NOAA Office for Coastal Management, 2015). Meffe et al. (2002) propose five major categories of stakeholders: (1) “people who live, work, play or worship at or near a resource”; (2) “people interested in the resource, its users, its use, or its non-use”; (3) “people interested in the processes used to make decisions”; (4) “people who pay the bills”; and (5) “people who represent citizens or are legally responsible for public resources.” Though not mutually exclusive, these different categories of stakeholders may require different forms of engagement and communication within the context of a transdisciplinary project.
Stakeholder engagement (sometimes called participatory processes, participatory management, or participatory programs) acknowledges the important effect of public perceptions, beliefs, and knowledge on issues such as resource management and incorporates processes, strategies, and mechanisms that proactively involve these nonexpert groups in projects and decision-making (NOAA Office for Coastal Management, 2015). The value of stakeholder engagement has become increasingly recognized over the past several decades, and stakeholder engagement is now a fundamental component of many agency operations in the 21st century (DiStaso, 2015; NOAA Office for Coastal Management, 2015). As discussed and reported across a range of projects, there are numerous benefits of stakeholder engagement, including providing local knowledge about a natural resource or a system and verifying researcher assumptions; improving scientific data quality and credibility; generating better decisions or outcomes and fostering support; building and maintaining trust; increasing understanding, acceptance, dissemination, and effective application of project results; and cultivating relationships and future collaborative opportunities (Jakeman, Letcher, & Norton, 2006; Roux, Rogers, Biggs, Ashton, & Sergeant, 2006; McNie, 2007; Liu, Gupta, Sprinter, & Wagener, 2008; Frazier, Wood, & Yarnal, 2010; Hage, Leroy, & Petersen, 2010; Voinov & Bousquet, 2010; Phillipson et al., 2012; Bartels et al., 2013; Podesta, Natenzon, Hidalgo, & Toranzo, 2013; NOAA Office for Coastal Management, 2015; Stephens DeLorme, & Hagen, 2015; Thompson, Lemieux, & Davis, 2015). There are also potential challenges with stakeholder engagement that should be considered, such as being laborious, communication-intensive, time-consuming, and costly (NOAA Office for Coastal Management, 2015).
A plethora of stakeholder engagement mechanisms, techniques, methods, and activities have been presented and reported in multiple disciplinary literature streams and in an array of technical reports (Lynam, de Jong, Sheil, Kusumanto, & Evans, 2007; Lauber, Decker, & Knuth, 2008; Reed et al., 2008;Reed, Graves, Dandy, & Posthumus, 2009; Hage et al., 2010; Allen et al., 2013). Selecting appropriate (i.e., efficient and effective) engagement mechanisms depends on the issue, situation, and stakeholders (NOAA Office for Coastal Management, 2015). To enhance accessibility and inclusion, projects often incorporate a combination of different engagement activities as each can offer particular strengths for different situations (Jacobs, Garfin, & Lenart, 2005). While there is no universal best approach, there is general consensus among practitioners and scholars that effective stakeholder engagement entails: identifying the appropriate stakeholders; communicating expectations clearly; proactively facilitating early, fair, and frequent multi-directional interaction throughout the life of the project from initiation to completion; and developing a positive and constructive “science-management” collaborative and inclusive learning partnership in which various perspectives and iterative feedback are encouraged and considered (Dalton, 2005; Jacobs et al., 2005; Jakeman et al., 2006; Roux et al., 2006; Liu et al., 2008; Reed et al., 2008; Phillipson et al., 2012; Bartels et al., 2013; Podesta et al., 2013; NOAA Office for Coastal Management, 2015; Thompson et al., 2015; DeLorme, Kidwell, Hagen, & Stephens, 2016).
Four types of stakeholder engagement mechanisms deemed especially appropriate and effective for transdisciplinary research and climate change-related projects include advisory committees, workshops, focus groups, and webinars (Hossain & Wigand, 2004; Jacobs et al., 2005; Liu et al., 2008; Voinov & Bousquet, 2010; Moser & Ekstrom, 2011; Halofsky et al., 2012; Allen et al., 2013; Ernst & van Riemsdijk, 2013; Kragt, Robson, & Macleod, 2013; Podesta et al., 2013; Thompson et al., 2015; DeLorme et al., 2016). Advisory committees are small groups of stakeholders representing various interests that are selected and established for a specified period of time to provide input, feedback, and guidance on certain aspects of a project or on certain decisions (NOAA Office for Coastal Management, 2015). Advisory committees can operate in a range of capacities and offer particular strengths of local knowledge and technical expertise, as well as boost project credibility. Workshops are small (i.e., fewer than 25 people) gatherings of selected stakeholders that are designed to complete a specific objective in a relatively short period of time (NOAA Office for Coastal Management, 2015). As a standard yet flexible form of engagement, workshops have been convened and conducted successfully for a spectrum of purposes. According to the literature, the strengths of this technique include a familiar process and a setting for forming relationships, facilitating dialogue, and encouraging learning about a project or issue (Jacobs et al., 2005; Moser & Ekstrom, 2011; Halofsky et al., 2012; Picketts et al., 2012; Allen et al., 2013; Thompson et al., 2015). Focus groups are a longstanding qualitative social science interviewing method that offers strengths in capturing spontaneous remarks, detailed firsthand descriptions, and contextual nuances (Krueger & Casey, 2000; Berg & Lune, 2012; Stewart & Shamdasani, 2015). Focus groups have been conducted successfully as a form of stakeholder engagement in transdisciplinary research and other projects addressing various environmental issues (Eisenhauer & Nicholson, 2006; Frazier et al., 2010). Webinars are conference calls that are enhanced by virtual presentation through software (Hossain & Wigand, 2004; Ernst & van Riemsdijk, 2013). With strengths of advanced technology, webinars can support stakeholder engagement by enabling synchronous communication, building trust, and preparing participants for related face-to-face project activities and meetings (Hossain & Wigand, 2004; Ernst & van Riemsdijk, 2013).
Evaluation of stakeholder engagement is advised as it can provide useful feedback for accountability as well as benchmarks and diagnostic insights for future planning. Due to the diversity of stakeholder engagement situations, however, there are no universal procedures to follow for evaluating success. Two common and broad criteria to consider for evaluation are process criteria (e.g., a workshop satisfaction survey that measures opportunity to provide input) and short- or long-term outcome criteria (e.g., helping to achieve overall project objectives such as development of a dynamic SLR impact model) (NOAA Office for Coastal Management, 2015). Like evaluation of other components of transdisciplinary projects, evaluation of stakeholder engagement is best considered an ongoing process and requires proper planning and preparation for optimal data collection and accurate results.
The authors would like to thank Soupy Dalyander and the anonymous reviewer for their constructive comments. This effort was funded in part under awards NA10NOS4780146 and NA16NOS4780208 from the National Oceanic and Atmospheric Administration (NOAA) Center for Sponsored Coastal Ocean Research (CSCOR) and the Louisiana Sea Grant Laborde Chair. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Akumu, C. E., Pathirana, S., Baban, S., & Bucher, D. (2011). Examining the potential impacts of sea level rise on coastal wetlands in north-eastern NSW, Australia. Journal of Coast Conservation, 15, 15–22.Find this resource:
Alizad, K., Hagen, S. C., Morris, J. T., Bacopoulos, P., Bilskie, M. V., Weishampel, J. F., & Medeiros, S. C. (2016). A coupled, two-dimensional hydrodynamic marsh model with biological feedback. Ecological Modeling, 327, 29–43.Find this resource:
Allen, E., Kruger, C., Leung, F., & Stephens, J. C. (2013). Diverse perceptions of stakeholder engagement within an environmental modeling research team. Journal of Environmental Studies and Science, 3, 343–356.Find this resource:
Atkinson, J. H., Smith, J. M., & Bender, C. (2013). Sea-level rise effects on storm surge and nearshore waves on the Texas coast: Influence of landscape and storm characteristics. Journal of Waterway, Port, Coastal, and Ocean Engineering, 139(2), 98–117.Find this resource:
Barbier, E. (2012). Progress and challenges in valuing coastal and marine ecosystem services. Review of Environmental Economics and Policy, 6(1), 1–19.Find this resource:
Barbier, E., Georgiou, I. Y., Enchelmeyer, B., & Reed, D. J. (2013). The value of wetlands in protecting Southeast Louisiana from hurricane storm surges. PLoS ONE, 8(3), e58715.Find this resource:
Barbier, E. B. (2014). A global strategy for protecting vulnerable coastal populations. Science, 345(6202), 1250–1251.Find this resource:
Barbier, E. B., Hacker, S. D., Kennedy, C., Koch, E. W., Stier, A. C., & Silliman, B. R. (2011). The value of estuarine and coastal ecosystem services. Ecological Monographs, 81(2), 169–193.Find this resource:
Bartels, W., Furman, C. A., Diehl, D. C., Royce, F. S., Dourte, D. R., Ortiz, B. V., … Jones, J. W. (2013). Warming up to climate change: A participatory approach to engaging with agricultural stakeholders in the Southeast US. Regional Environmental Change, 13(Suppl. 1), S45–S55.Find this resource:
Baustian, J. J., & Mendelssohn, I. A. (2015). Hurricane-induced sedimentation improves marsh resilience and vegetation vigor under high rates of relative sea level rise. Wetlands, 35, 795–802.Find this resource:
Berg, B. L., & Lune, H. (2012). Qualitative research methods for the social sciences. Boston: Pearson.Find this resource:
Bilskie, M. V., Hagen, S. C., Alizad, K., Medeiros, S. C., Passeri, D. L., Needham, H. F., & Cox, A. (2016a). Dynamic simulation and numerical analysis of hurricane storm surge under sea level rise with geomorphologic changes along the northern Gulf of Mexico. Earth’s Future, 4(5), 177–193.Find this resource:
Bilskie, M. V., Hagen, S. C., Medeiros, S. C., Cox, A. T., Salisbury, M., & Coggin, D. (2016b). Data and numerical analysis of astronomic tides, wind-waves, and hurricane storm surge along the northern Gulf of Mexico. Journal of Geophysical Research: Oceans, 121(5), 3625–3658.Find this resource:
Bilskie, M. V., Hagen, S. C., Medeiros, S. C., & Passeri, D. L. (2014). Dynamics of sea level rise and coastal flooding on a changing landscape. Geophysical Research Letters, 41(3), 927–934.Find this resource:
Bindoff, N. L., Willebrand, J., Artale, V., Cazenave, A., Gregory, J. et al. (2007). Observations: Ocean climate and sea level. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, et al. (Eds.), Climate change 2007: The physical science basis. Contribution of the Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 385–432). Cambridge, U.K.: Cambridge University Press.Find this resource:
Cahoon, D. R., Reed, D. J., Day, J. W., Steyer, G. D., Boumans, R. M., Lynch, J. C., … Latif, N. (1995). The influence of Hurricane Andrew on sediment distribution in Louisiana coastal marshes. Journal of Coastal Research, SI(21), 280–294.Find this resource:
Carney, S., Whitmarsh, L., Nicholson-Cole, S. A., & Shackley, S. (2009). A dynamic typology of stakeholder engagement within climate change research. Tyndall Working Paper 128. East Anglia UK: Tyndall Centre for Climate Change Research.Find this resource:
Cazenave, A., & Le Cozannet, G. (2013). Sea level rise and its coastal impacts. Earth’s Future, 2, 15–34.Find this resource:
Chen, Q., Wang, H., Wang, L., Tawes, R., & Rollman, D. (2014). Predicting impacts of tropical cyclones and sea-level rise on beach mouse habitat. Journal of Coastal Research, SI(68), 12–19.Find this resource:
Church, J. A., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S., Levermann, A., … Unnikrishnan, A. S. (2013). Sea level change. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1137-1216). Cambridge, U.K.: Cambridge University Press.Find this resource:
Church, J. A., & White, N. J. (2006). A 20th century acceleration in global sea-level rise. Geophysical Research Letters, 33(1), L01602.Find this resource:
Church, J. A., & White, N. J. (2011). Sea-level rise from the late 19th to early 21st century. Surveys in Geophysics, 32(4–5), 585–602.Find this resource:
Costanza, R., Perez-Maqueo, O., Martinez, M. L., Sutton, P., Anderson, S., & Mulder, K. (2008). The value of coastal wetlands for hurricane protection. AMBIO: A Journal of the Human Environment, 37(4), 241–248.Find this resource:
Cowell, P. J., Roy, P. S., & Jones, R. A. (1995). Simulation of large-scale coastal change using a morphological behaviour model. Marine Geology, 126, 45–61.Find this resource:
Cowell, P. J., Stive, M. J. F., Niedoroda, A. W., De Vriend, H. J., Swift, D. J. P., Kaminsky, G. M., & Capobianco, M. (2003a). The coastal tract: Part 1: A conceptual approach to aggregated modelling of low-order coastal change. Journal of Coastal Research, 19, 812–827.Find this resource:
Cowell, P. J., Stive, M. J. F., Niedoroda, A. W., Swift, D. J. P., De Vriend, H. J., Buisjman, M. C., … P.L. de Boer (2003b). The coastal tract. Part 2: Applications of aggregated modelling of lower-order coastal change. Journal of Coastal Research, 19, 828–848.Find this resource:
Dalton, T. M. (2005). Beyond biogeography: A framework for involving the public in planning of U.S. marine protected areas. Conservation Biology, 19(5), 1392–1401.Find this resource:
DeLorme, D. E., Kidwell, D., Hagen, S. C., & Stephens, S. H. (2016). Developing and managing transdisciplinary and transformative research on the coastal dynamics of sea level rise: Experiences and lessons learned. Earth’s Future, 4, 194–209.Find this resource:
DiStaso, M. W. (2015). Ethical stakeholder engagement. Public Relations Journal, 9(1). Available at http://www/prsa.org/Intelligence/PRJournal/Vol9/No1.Find this resource:
Eisenhauer, B. W., & Nicholson, B. (2006). Using stakeholders’ views: A social science methodology for the inclusive design of environmental communications. Applied Environmental Education and Communication, 4, 19–30.Find this resource:
Ernst, K. M., & van Riemsdijk, M. (2013). Climate change scenario planning in Alaska’s national parks: Stakeholder involvement in the decision-making process. Applied Geography, 45, 22–28.Find this resource:
Farber, S., Costanza, R., Childers, D. L., Erickson, J., Gross, K., Grove, M., … Wilson, M. (2006). Linking ecology and economics for ecosystem management. BioScience, 56(2), 117–129.Find this resource:
Feagin, R. A., Sherman, D. J., & Grant, W. E. (2005). Coastal erosion, global sea-level rise, and the loss of sand dune plant habitats. Frontiers in Ecology and the Environment, 3, 359–364.Find this resource:
Fearnley, S. M., Miner, M. D., Kulp, M., Bohling, C., & Penland, S. (2009). Hurricane impact and recovery shoreline change analysis of the Chandeleur Islands, Louisiana, USA. Geo-Marine Letters, 29, 455–466.Find this resource:
Fitzgerald, D. M., Fenster, M. S., Argow, B. A., & Buynevich, I. V. (2008). Coastal impacts due to sea level rise. Annual Review Earth Planet Science, 36, 601–647.Find this resource:
Frazier, T. G., Wood, N., & Yarnal, B. (2010). Stakeholder perspectives on land-use strategies for adapting to climate-change-enhanced coastal hazards: Sarasota, Florida. Applied Geography, 30, 506–517.Find this resource:
Friedrichs, C. T., & Perry, J. E. (2001). Tidal salt marsh morphodynamics: A synthesis. Journal of Coastal Research, SI 27, 7-37.Find this resource:
Geden, K. B., Kirwan, M. L., Wolanski, E., Barbier, E. B., & Silliman, B. R. (2011). The present and future role of coastal wetland vegetation in protecting shorelines: answering recent challenges to the paradigm. Climatic Change, 106, 7–29.Find this resource:
Gesch, D. B. (2009). Analysis of lidar elevation data for improved identification and delineation of lands vulnerable to sea level rise. Journal of Coastal Research, Special Issue(53), 49–58.Find this resource:
Gesch, D. B., Gutierrez, B. T., & Gill, S. K. (2009). Coastal elevations. In Coastal sensitivity to sea-level rise: A focus on the mid-Atlantic region (pp. 25–42). Washington, DC: U.S. Climate Change Science Program.Find this resource:
Gieder, K. D., Karpanty, S. M., Fraser, J. D., Catlin, D. H., Gutierrez, B. T., Plant, N. G., … Thieler, E. R. (2014). A Bayesian network approach to predicting nest presence of the federally-threatened piping plower (Charadrius melodus) using barrier island features. Ecological Modeling, 276, 38–50.Find this resource:
Gouldby, B., & Samuels, P. (2005). Language of Risk—Project Definitions, Rep. T32-04-01, Floodsite Consortium, Wallingford UK.Find this resource:
Gutierrez, B. T., Plant, N. G., & Thieler, E. R. (2011). A Bayesian network to predict coastal vulnerability to sea level rise. Journal of Geophysical Research, 116, (FO2009), 1-15.Find this resource:
Gutierrez, B. T., Plant, N. G., Thieler, E. R., & Turecek, A. (2015). Using a Bayesian network to predict barrier island geomorphologic characteristics. Journal of Geophysical Research: Earth Surface, 120, 2452-2475.Find this resource:
Gutierrez, B. T., Williams, S. J., & Thieler, E. R. (2009). Ocean coasts. In Coastal sensitivity to sea-level rise: A focus on the mid-Atlantic region (pp. 43–56). Washington, DC: U.S. Climate Change Science Program.Find this resource:
Hage, M., Leroy, P., & Petersen, A. C. (2010). Stakeholder participation in environmental knowledge production. Futures, 42(3), 254–264.Find this resource:
Hagen, S. C., & Bacopoulos, P. (2012). Synthetic storms contributing to coastal flooding in Florida’s Big Bend region with application to sea level rise. Terrestrial, Atmospheric and Oceanic Sciences, 23(5), 481–500.Find this resource:
Hagen, S. C., Morris, J. T., Bacopoulos, P., & Weishampel, J. F. (2013). Sea-level rise impact on a salt marsh system of the lower St. Johns River. Journal of Waterway, Port, Coastal and Ocean Engineering, J. Waterway, Port, Coastal, Ocean Eng., 139(2), 118–125.Find this resource:
Halofsky, J. E., Peterson, D. L., Furniss, M. J., Joyce, L. A., Millar, C. I., & Nielson, R. P. (2012). Workshop approach for developing climate change adaptation strategies and actions for natural resource management agencies in the United States. Journal of Forestry, 109, 219–225.Find this resource:
Hapke, C., & Plant, N. (2010). Predicting coastal cliff erosion using a Bayesian probabilistic model. Marine Geology, 278, 140–149.Find this resource:
Hay, C. C., Morrow, E., Kopp, R. E., & Mitrovica, J. X. (2015). Probabilistic reanalysis of twentieth-century sea-level rise. Nature, 517, 481–484.Find this resource:
Hossain, L., & Wigand, R. T. (2004). ICT enabled virtual collaboration through trust. Journal of Computer-mediated Communication, 10(1), 1–10.Find this resource:
IMPLAN Group, L. (2013). IMPLAN Professional (Version 3.0) [computer software]. Available from http://www.implan.com.
IPCC (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, & L. L. White. Cambridge, U.K.: Cambridge University Press.Find this resource:
IPCC (2013). Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T. F. Stocker, D. Qin, G.‑K. Plattner et al. Cambridge, U.K.: Cambridge University Press. PUBLISHER.Find this resource:
Jacobs, K., Garfin, G., & Lenart, M. (2005). More than just talk: Connecting science and decisionmaking. Environment: Science and Policy for Sustainable Development, 47(9), 7–21.Find this resource:
Jakeman, A. J., Letcher, R. A., & Norton, J. P. (2006). Ten iterative steps in development and evaluation of environmental models. Environmental Modelling and Software, 21(5), 602–614.Find this resource:
Jevrejeva, S., Grinsted, A., Moore, J., & Holgate, S. (2006). Nonlinear trends and multiyear cycles in sea level records. Journal of Geophysical Research, 111, 1–11.Find this resource:
Jevrejeva, S., Moore, J. C., Grinsted, A., & Woodworth, P. L. (2008). Recent global sea level acceleration started over 200 years ago?Geophysical Research Letters, 35(L08715).Find this resource:
Jorgensen, S. E., & Fath, B. D. (2011). 10-Structurally dynamic models. Developments in Environmental Modeling, 23, 309–346.Find this resource:
Keating, C. B. (2008). Emergence in system of systems. In M. Jamshidi (Ed.), System of systems engineering (pp. 169–190). Hoboken, N.J.: John Wiley & Sons.Find this resource:
Kirwan, M. L., Temmerman, S., Skeehan, E. E., Guntenspergen, G. R., & Fagherazzi, S. (2016). Overestimation of marsh vulnerability to sea level rise. Nature Climate Change, 6, 253–260.Find this resource:
Kragt, M. E., Robson, B., & Macleod, C. J. A. (2013). Modelers’ roles in structuring integrative research projects. Environmental Modelling and Software, 39, 322–330.Find this resource:
Krueger, R. A., & Casey, M. A. (2000). Focus groups: A practical guide for applied research. Thousand Oaks, CA: SAGE.Find this resource:
Kummu, M., de Moel, H., Salvucci, G., Viviroli, D., Ward, P. J., & Varis, O. (2016). Over the hills and further away from the coast: Global geospatial patterns of human and environment over the 20th–21st centuries. Environmental Research Letters, 11, 1–15.Find this resource:
Lauber, T. B., Decker, D. J., & Knuth, B. A. (2008). Social networks and community-based natural resource management. Environmental Management, 42, 677–687.Find this resource:
Lemos, M. C., Kirchoff, C. J., & Ramprasad, V. (2012). Narrowing the climate information usability gap. Nature Climate Change, 2, 789–794.Find this resource:
Levermann, A., Clark, P. U., Marzeion, B., Milne, G. A., Pollard, D., Radic, V., & Robinson, A. (2013). The multimillenial sea level commitment of global warming. Proceedings of National Academy of Science USA, 110(3), 13745–13750.Find this resource:
Levy, P. (2011). Essentials of trandisciplinary research: Using problem-centered methodologies. Walnut Creek, CA: Left Coast Press.Find this resource:
Liu, Y., Gupta, H., Springer, E., & Wagener, T. (2008). Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling and Software, 23(7), 846–858.Find this resource:
Long, J. W., de Bakker, A. T. M., & Plant, N. G. (2014). Scaling coastal dune elevation changes across storm-impact regimes. Geophysical Research Letters, 41, 1–8.Find this resource:
Lorenzo-Trueba, J., & Ashton, A. D. (2014). Rollover, drowning and discontinuous retreat: Distinct modes of barrier response to sea-level rise arising from a simple morphodynamic model. Journal of Geophysical Research Earth Surface, 119, 779–801.Find this resource:
Lynam, T., de Jong, W., Sheil, D., Kusumanto, T., & Evans, K. (2007). A review of tools for incorporating community knowledge, preferences, and values into decision making in natural resource management. Ecology and Society, 12(1). Online.Find this resource:
Maier, M. W. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267–284.Find this resource:
Mariotti, G., & Fagherazzi, S. (2010). A numerical model for the coupled long-term evolution of salt marshes and tidal flats. Journal of Geophysical Research, 115.Find this resource:
Masterson, J. P., Fienen, M. N., Thieler, E. R., Gesch, D. B., Gutierrez, B. T., & Plant, N. G. (2013). Effects of sea-level rise on barrier island groundwater system dynamics: Ecohydrological implications. Ecohydrology, 7(3), 1064–1071.Find this resource:
McCall, R. T., Van Thiel de Vries, J., Plant, N. G., Van Dongeren, A., Thompson, D. M., & Reniers, A. (2010). Two-dimensional time dependent hurricane overwash and erosion modeling at Santa Rosa Island. Coastal Engineering, 57, 668–683.Find this resource:
McKee, K. L., & Cherry, J. A. (2009). Hurricane Katrina sediment slowed elevation loss in subsiding brackish marshes of the Mississippi River Delta. Wetlands, 29, 2–5.Find this resource:
McNie, E. C. (2007). Reconciling the supply of scientific information with user demands: An analysis of the problem and review of the literature. Environmental Science & Policy, 10, 17–38.Find this resource:
Meffe, G. K., Nielsen, L. A., Knight, R. L., & Schenborn, D. A. (2002). Ecosystem management: Adaptive, community-based conservation. Washington, DC: Island Press.Find this resource:
Moller, I., Kudella, M., Rupprecht, F., Spencer, T., Paul, M., van Wesenbeeck, B. K., … Schimmels, S. (2014). Wave attenuation over coastal salt marshes under storm surge conditions. Nature Geoscience, 7(10), 727–731.Find this resource:
Moller, I., Spencer, T., French, J. R., Leggett, D. J., & Dixon, M. (1999). Wave transformation over salt marshes: A field and numerical modelling study from North Norfolk, England. Estuarine, Coastal and Shelf Science, 49(3), 411–426.Find this resource:
Morris, J. T., Barber, D. C., Callaway, J. C., Chambers, R., Hagen, S. C., Hopkinson, C. S., … Wigand, C. (2016). Contributions of organic and inorganic matter to sediment volume and accretion in tidal wetlands at steady state. Earth’s Future, 4, 110–121.Find this resource:
Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B., & Cahoon, D. R. (2002). Responses of coastal wetlands to rising sea level. Ecology, 83(10), 2869–2877.Find this resource:
Morton, R. A., Miller, T. L., & Moore, L. J. (2004). National assessment of shoreline change: Part 1: Historical shoreline changes and associated coastal land loss along the U.S. Gulf of Mexico. Open-file Report 2004-1043. St. Petersburg, FL: Geological Survey, 1–45.Find this resource:
Moser, S. A., & Ekstrom, J. A. (2011). Taking ownership of climate change: Participatory adaptation planning in two local case studies from California. Journal of Environmental Studies and Science, 1(1), 63–74.Find this resource:
Mudd, S. M., D’Alpaos, A., & Morris, J. T. (2010). How does vegetation affect sedimentation on tidal marshes? Investigation particle capture and hydrodynamic controls on biologically mediated sedimentation. Journal of Geophysical Research, 115, 1–14.Find this resource:
Nerem, R. S., Chambers, D., Choe, C., & Mitchum, G. T. (2010). Estimating mean sea level change from the TOPEX and Jason Altimeter Missions. Marine Geodesy, 33(1), 435.Find this resource:
NOAA (2011). The Gulf of Mexico at a glance: A second glance. Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration.Find this resource:
NOAA (2012). Spatial trends in coastal socioeconomics demographic trends database: 1970–2010. Available at http://coastalsocioeconomics.noaa.gov/.Find this resource:
NOAA (2013). Population trends from 1970 to 2020. National Coastal Population Report: 22.Find this resource:
NOAA (2015). NCEI billion-dollar weather and climate disasters: Table of events. Available from https://www.ncdc.noaa.gov/billions/events.Find this resource:
NOAA Office for Coastal Management (2015). Introduction to stakeholder participation. Available at http://www.coast.noaa.gov.Find this resource:
NRC (2012). Approaches for ecosystem services valuations for the Gulf of Mexico after the deepwater horizon oil spill. National Academies Press: 128.Find this resource:
Nyman, J. A., Walters, R. J., Delaune, R. D., & Patrick, W. H., Jr. (2006). Marsh vertical accretion via vegetative growth. Estuarine, Coastal and Shelf Science, 69, 370–380.Find this resource:
OSTP (2015). Ecosystem-service assessment: Research needs for coastal green infrastructure. Available from https://www.whitehouse.gov/sites/default/files/microsites/ostp/cgies_research_agenda_final_082515.pdf.:1-40.Find this resource:
Ouyang, X., & Lee, S. Y. (2014). Updated estimates of carbon accumulation rates in coastal marsh sediments. Biogeosciences, 11, 5057–5071.Find this resource:
Parris, A., Bromirski, P., Burkett, V., Cayan, D., Culver, M., Hall, J., … Weiss, J. (2012). Global sea level rise scenarios for the United States National Climate Assessment. NOAA Tech Memo OAR CPO-1, p. 37.Find this resource:
Passeri, D. L., Hagen, S. C., Bilskie, M. V., & Medeiros, S. C. (2015a). On the significance of incorporating shoreline changes for evaluating coastal hydrodynamics under sea level rise scenarios. Natural Hazards, 75(2), 1599–1617.Find this resource:
Passeri, D. L., Hagen, S. C., Medeiros, S. C., & Bilskie, M. V. (2015b). Impacts of historic morphological changes and sea level rise on tidal hydrodynamics in a microtidal estuary (Grand Bay, MS). Continental Shelf Research, 111, Part B, 150–158.Find this resource:
Passeri, D. L., Hagen, S. C., Medeiros, S. C., Bilskie, M. V., Alizad, K., & Wang, D. (2015c). The dynamic effects of sea level rise on low-gradient coastal landscapes: A review. Earth’s Future, 3(6), 159–181.Find this resource:
Passeri, D. L., Hagen, S. C., Plant, N. G., Bilskie, M. V., Medeiros, S. C., & Alizad, K. (2016). Tidal hydrodynamics under future sea level rise and coastal morphology in the northern Gulf of Mexico. Earth’s Future, 4(5), 159–176.Find this resource:
Phillips, J. D. (1986). Coastal submergence and marsh fridge erosion. Journal of Coastal Research, 2, 427–436Find this resource:
Phillipson, J., Lowe, P., Proctor, A., & Ruto, E. (2012). Stakeholder engagement and knowledge exchange in environmental research. Journal of Environmental Management, 95, 56–65.Find this resource:
Picketts, I. M., Wemer, A. T., Murdock, T. Q., Curry, J., Dery, S. J., & Dyer, D. (2012). Planning for climate change adaptation: Lessons learned from a community-based workshop. Environmental Science & Policy, 17, 82–93.Find this resource:
Plant, N. G., & Stockdon, H. F. (2012). Probablistic prediction of barrier-island response to hurricanes. Journal of Geophysical Research, 117(F03015).Find this resource:
Plant, N. G., Thieler, E. R., & Passeri, D. L. (2016). Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network. Earth’s Future, 4(5), 143–158.Find this resource:
Podesta, G. P., Natenzon, C. E., Hidalgo, C., & Toranzo, F. R. (2013). Interdisciplinary production of knowledge with participation of stakeholders: A case study of a collaborative project on climate variability, human decisions, and agricultural ecosystems in the Argentine Pampas. Environmental Science & Policy, 26, 40–48.Find this resource:
Reece, J. S., Passeri, D., Ehrhart, L., Hagen, S., Hays, A., Long, C., … Wolf, S. (2013). Sea level rise, land use, and climate change influence the distribution of loggerhead turtle nests at the largest USA rookery (Melbourne Beach, Florida). Marine Ecology Progress Series, 493, 259–274.Find this resource:
Reed, D. J. (1990). The impact of sea level rise on coastal marshes. Progress in Physical Geology, 14(4), 465–481.Find this resource:
Reed, D. J., Bishara, D. A., Cahoon, D. R., Donnelly, J., Kearney, M., Kolker, A. S., … Stevenson, J. C. (2008). Site specific scenarios for wetlands accretion as sea level rises in the mid-Atlantic region. In J. G. Titus & E. M. Strange (Eds.), Background documents supporting climate change science program synthesis and assessment product 4.1 (section 2.1). EPA 430R07004. Washington, DC: EPA.Find this resource:
Reed, M.S., Graves, A., Dandy, N. & Posthumus, H. (2009). Who’s in and why? A typology of stakeholder analysis methods for natural resource management. Journal of Environmental Management, 90, 1933–1949.Find this resource:
Roelvink, J. A., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., & Lescinski, J. (2009). Modeling storm impacts on beaches, dunes and barrier islands. Coastal Engineering, 56, 1133–1152.Find this resource:
Roux, D. J., Rogers, K. H., Biggs, H. C., Ashton, P. J., & Sergeant, A. (2006). Bridging the science–management divide: Moving from unidirectional knowledge transfer to knowledge interfacing and sharing. Ecology and Society., 11(1), 4.Find this resource:
Sallenger, A. H. J. (2000). Storm impact scale for barrier islands. Journal of Coastal Research, 16(3), 890–895.Find this resource:
Sampath, D. M. R., & Boski, T. (2016). Morphological response of the saltmarsh habitats of the Guadiana estuary due to flow regulation and sea-level rise. Estuarine, Coastal and Shelf Science, 183, Part B, 314–326.Find this resource:
Silliman, B. R., & Bertness, M. D. (2002). A tropic cascade regulates salt marsh primary production. Proceedings of National Academy of Science, 99, 10500–10505.Find this resource:
Smith, J. M., Cialone, M. A., Wamsley, T. V., & McAlpin, T. O. (2010). Potential impact of sea level rise on coastal surges in southeast Louisiana. Ocean Engineering, 37, 37–47.Find this resource:
Stephens, S. H., DeLorme, D. E., & Hagen, S. C. (2015). Evaluating the utility and communication effectiveness of an interactive sea level rise viewer through stakeholder engagement. Journal of Business and Technical Communication, 29(3), 314–343.Find this resource:
Stewart, D. W., & Shamdasani, P. N. (2015). Focus groups: Theory and practice. Los Angeles, CA: SAGE.Find this resource:
Stockdon, H. F., Doran, K. J., Thompson, D. M., Sopkin, K. L., Plant, N. G., & Sallenger, A. H. (2012). National assessment of hurricane-induced coastal erosion hazards-Gulf of Mexico. U.S. Geological Survey Open-File Report 2012–1084, 1-51.Find this resource:
Stockdon, H. F., Sallenger, A. H., Holman, R., & Howd, P. (2007). A simple model for the spatially-variable coastal response to hurricanes. Marine Geology, 238, 1–20.Find this resource:
Stolper, D., List, J. H., & Thieler, E. R. (2005). Simulating the evolution of coastal morphology and straigraphy with a new morphological-behavior model (GEOMBEST). Marine Geology, 218(1–4), 17–36.Find this resource:
Storms, J. E. A., Weltje, G. J., van Dijke, J. J., Geel, C. R., & Kroonenberg, S. B. (2002). Process-response modeling of wave-dominated coastal systems: Simulating evolution and stratigraphy on geological timescales. Journal of Sedimentary Research, 72(2), 226–239.Find this resource:
Stralberg, D., Brennan, M., Callaway, J. C., Wood, J. K., Schile, L. M., Jongsomjit, D., … Crooks, S. (2011). Evaluating tidal marsh sustainability in the face of sea-level rise: a hybrid modeling approach applied to San Francisco Bay. PLoS ONE, 6(11), e27388.Find this resource:
Thompson, J. L., Lemieux, C. J., & Davis, S. (2015). Climate change adaptation planning: An analysis of process and practice in two regional case studies. 2015 Conference on Communication and Environment. Boulder, CO: International Environmental Communication Association.Find this resource:
Townend, I., Fletcher, C., Knappen, M., & Rossington, K. (2011). A review of salt marsh dynamics. Water and Environment Journal, 25, 477–488.Find this resource:
Tribbia, J., & Moser, S. C. (2008). More than information: What coastal managers need to plan for climate change. Environmental Science & Policy, 11(4), 315–328.Find this resource:
Twichell, D. C., Flocks, J. G., Pendleton, E. A., & Baldwin, W. E. (2013). Geologic controls on regional and local erosion rates of three Northern Gulf of Mexico barrier-island systems. Journal of Coastal Research, SI-63, 33–45.Find this resource:
Twichell, D. C., Pendleton, E. A., Baldwin, W. E., & Flocks, J. G. (2009). Subsurface control on seafloor erosional processes offshore of the Chandeleur Islands, Louisiana. Geo-Marine Letters, 29(6), 349–358.Find this resource:
Voinov, A., & Bousquet, F. (2010). Modeling with stakeholders. Environmental Modeling and Software, 25, 1268–1281.Find this resource:
Wahl, T., & Plant, N. G. (2015). Changes in erosion and flooding risk due to long-term and cyclic oceanographic trends. Geophysical Research Letters, 42(8), 2943–2950.Find this resource:
Wickson, F., Carew, A. L., & Russell, A. W. (2006). Transdisciplinary research: characteristics, quandaries and quality. Futures, 38(9), 1046–1059.Find this resource:
Wong, P. P., Losada, I. J., Gattuso, J. P., Hinkel, J., Khattabi, A., McInnes, K. L., … Sallenger, A. (2014). Coastal systems and low-lying areas. In C. B. Field, V. R. Barros, D. J. Dokken et al. (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 361–409). Cambridge, U.K.: Cambridge University Press.Find this resource:
Yates, M. L., & Le Cozannet, G. (2012). Evaluating European coastal evolution using Bayesian Networks. Natural Hazards Earth System Sciences, 12, 1173–1177.Find this resource:
Yoskowitz, D., & Russell, M. (2015). Human dimensions of our estuaries and coasts. Estuaries and Coasts, 38(SI), 1–8.Find this resource:
Yoskowitz, D., Werner, S., Carollo, C., Santos, C., Washburn, T., & Isaksen, G. (2016). Gulf of Mexico offshore ecosystem services: Relative valuation by stakeholders. Marine Policy, 66, 132–136.Find this resource:
Zhang, K., Douglas, B. C., & Leatherman, S. P. (2004). Global warming and coastal erosion. Climatic Change, 64(1/2), 41–58.Find this resource:
Zhang, K., Li, Y., Liu, H., Xu, H., & Shen, J. (2013). Comparison of three methods for estimating the sea level rise effect on storm surge flooding. Climatic Change, 118(2), 487–500.Find this resource: