Global sea level rise (SLR) may present the most urgent climate change adaptation challenge facing coastal communities today. The direction is clear, impacts are manifesting now, and the pace of rise is likely to accelerate. As a result, many coastal communities have begun planning their adaptation response and some are quite far along in the process.
At the same time, evolving science provides new observations, models, and understanding of land-ocean dynamics that can increase clarity while also in many ways increase uncertainty about the scope, timing, and regional nature of SLR. The planning, design, and construction of water infrastructure has a relatively long timeline (up to 30 years), and thus the evolution of scientific knowledge presents challenges for communities already planning for SLR based on previous information. When does science become actionable for decision-makers? Are there characteristics or thresholds that could cause communities decide to move from one set of scenarios to another, or change approaches altogether?
This talk focuses on two important studies different in kind but dominating the conversation about SLR adaptation planning today. First, DeConto and Pollard (2016) have suggested significantly higher upper end projections for Antarctic ice sheet melt, which increase both global and regional SLR above most previously assumed upper limits. Second, probabilistic projections using model output and expert elicitation as presented in Kopp et al (2014) are increasingly appearing in federal reports and planning-related documents. These two papers are pushing the boundaries of the science-to-planning interface, while the application of this work as actionable science is far from settled.
This talk will present the outcome of recent conversations among our diverse author team. The authors are engaged in SLR planning related contexts from many angles and perspectives and include the aforementioned Kopp and DeConto as well as representatives of the City of San Francisco, Army Corps of Engineers, Environmental Protection Agency, and engineering consultant community. Attendees of this session will hear a presentation demonstrating co-production in process, including topics about which the authors have and have not agreed upon to date, with some attention to next steps in the process.
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Using Virtual Reality in Sea Level Rise Planning and Community Engagement—An Overview
As coastal communities around the globe contend with the impacts of climate change including coastal hazards such as sea level rise and more frequent coastal storms, educating stakeholders and the general public has become essential in order to adapt to and mitigate these risks. Communicating SLR and other coastal risks is not a simple task. First, SLR is a phenomenon that is abstract as it is physically distant from many people; second, the rise of the sea is a slow and temporally distant process which makes this issue psychologically distant from our everyday life. Virtual reality (VR) simulations may offer a way to overcome some of these challenges, enabling users to learn key principles related to climate change and coastal risks in an immersive, interactive, and safe learning environment. This article first presents the literature on environmental issues communication and engagement; second, it introduces VR technology evolution and expands the discussion on VR application for environmental literacy. We then provide an account of how three coastal communities have used VR experiences developed by multidisciplinary teams—including residents—to support communication and community outreach focused on SLR and discuss their implications.
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- Award ID(s):
- 1906728
- NSF-PAR ID:
- 10252387
- Date Published:
- Journal Name:
- Water
- Volume:
- 13
- Issue:
- 9
- ISSN:
- 2073-4441
- Page Range / eLocation ID:
- 1142
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Despite increasing risks from sea-level rise (SLR) and storms, US coastal communities continue to attract relatively high-income residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as relatively high-income owners enter the market and self-finance nourishment. Our results suggest a policy tradeoff between slowing demographic transitions in coastal communities and allowing property markets to adjust smoothly to risks from climate change.
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Sea level rise (SLR) may impose substantial economic costs to coastal communities worldwide, but characterizing its global impact remains challenging because SLR costs depend heavily on natural characteristics and human investments at each location – including topography, the spatial distribution of assets, and local adaptation decisions. To date, several impact models have been developed to estimate the global costs of SLR. Yet, the limited availability of open-source and modular platforms that easily ingest up-to-date socioeconomic and physical data sources restricts the ability of existing systems to incorporate new insights transparently. In this paper, we present a modular, open-source platform designed to address this need, providing end-to-end transparency from global input data to a scalable least-cost optimization framework that estimates adaptation and net SLR costs for nearly 10 000 global coastline segments and administrative regions. Our approach accounts both for uncertainty in the magnitude of global mean sea level (g.m.s.l.) rise and spatial variability in local relative sea level rise. Using this platform, we evaluate costs across 230 possible socioeconomic and SLR trajectories in the 21st century. According to the latest Intergovernmental Panel on Climate Change Assessment Report (AR6), g.m.s.l. is likely to rise during the 21st century by 0.40–0.69 m if late-century warming reaches 2 ∘C and by 0.58–0.91 m with 4 ∘C of warming (Fox-Kemper et al., 2021). With no forward-looking adaptation, we estimate that annual costs of sea level rise associated with a 2 ∘C scenario will likely fall between USD 1.2 and 4.0 trillion (0.1 % and 1.2 % of GDP, respectively) by 2100, depending on socioeconomic and sea level rise trajectories. Cost-effective, proactive adaptation would provide substantial benefits, lowering these values to between USD 110 and USD 530 billion (0.02 and 0.06 %) under an optimal adaptation scenario. For the likely SLR trajectories associated with 4 ∘C warming, these costs range from USD 3.1 to 6.9 trillion (0.3 % and 2.0 %) with no forward-looking adaptation and USD 200 billion to USD 750 billion (0.04 % to 0.09 %) under optimal adaptation. The Intergovernmental Panel on Climate Change (IPCC) notes that deeply uncertain physical processes like marine ice cliff instability could drive substantially higher global sea level rise, potentially approaching 2.0 m by 2100 in very high emission scenarios. Accordingly, we also model the impacts of 1.5 and 2.0 m g.m.s.l. rises by 2100; the associated annual cost estimates range from USD 11.2 to 30.6 trillion (1.2 % and 7.6 %) under no forward-looking adaptation and USD 420 billion to 1.5 trillion (0.08 % to 0.20 %) under optimal adaptation. Our modeling platform used to generate these estimates is publicly available in an effort to spur research collaboration and support decision-making, with segment-level physical and socioeconomic input characteristics provided at https://doi.org/10.5281/zenodo.7693868 (Bolliger et al., 2023a) and model results at https://doi.org/10.5281/zenodo.7693869 (Bolliger et al., 2023b).more » « less
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Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise.more » « less
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It is generally acknowledged that interdependent critical infrastructure in coastal urban areas is constantly threatened by storm-induced flooding. Due to changing climate effects, such as sea level rise (SLR), the occurrence of catastrophic events will be more frequent and may trigger an increased likelihood of severe hazards. Planning a protective measure or mitigation strategy is a complex problem given the constraints that it must fit within a prescribed and limited fiscal budget and be beneficial to the community it protects both socially and economically. This article proposes a methodology for optimizing protective measures and mitigation strategies for interdependent infrastructures subjected to storm-induced flooding and climate change impacts such as SLR. Optimality is defined in this methodology as a maximum reduction in overall expected losses within a prescribed budget (compared to the expected losses in the case of doing nothing for protection/mitigation). Protective measures can include seawalls, barriers, artificial dunes, restoration of wetlands, raising individual buildings, sealing parts of the infrastructure, strategic retreat, insurance, and many more. The optimal protective strategy can be a combination of several protective measures implemented over space and time. The optimization process starts with parameterizing the protective measures. Storm-induced flooding and SLR, and their corresponding consequences, are estimated using a GIS-based subdivision-redistribution methodology (GISSR) developed by the authors for finding a rough solution in the first brute-force iterations of the optimization loop. A storm surge computational model called GeoClaw is subsequently used to simulate ensembles of synthetic storms in order to fine-tune and achieve the optimal solution. Damage loss, including economic impacts, is quantified based on calculated flood estimates. The suitability of the potential optimal solution is examined and assessed with input from stakeholders' interviews. It should be mentioned that the results and conclusions provided in this work depend on the assumptions made about future sea level rise (SLR). The authors acknowledge that there are other, more severe predictions for sea level rise (SLR), than the one used in this paper.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/w13091142
