Publications

Abstract:

Compound flooding, where the combination or successive occurrence of two or more flood drivers leads to an extreme impact, can greatly exacerbate the adverse consequences associated with flooding in coastal regions. This paper reviews the practices and trends in coastal compound flood research methodologies and applications, as well as synthesizes key findings at regional and global scales. Systematic review is employed to construct a literature database of 271 studies relevant to compound flood hazards in a coastal context. This review explores the types of compound flood events, their mechanistic processes, and synthesizes the definitions and terms exhibited throughout the literature. Considered in the review are six flood drivers (fluvial, pluvial, coastal, groundwater, damming/dam failure, and tsunami) and five precursor events and environmental conditions (soil moisture, snow, temp/heat, fire, and drought). Furthermore, this review summarizes the trends in research methodology, examines the wide range of study applications, and considers the influences of climate change and urban environments. Finally, this review highlights the knowledge gaps in compound flood research and discusses the implications of review findings on future practices. Our five recommendations for future compound flood research are to: 1) adopt consistent definitions, terminology, and approaches; 2) expand the geographic coverage of research; 3) pursue more inter-comparison projects; 4) develop modelling frameworks that better couple dynamic earth systems; and 5) design urban and coastal infrastructure with compound flooding in mind. We hope this review will help to enhance understanding of compound flooding, guide areas for future research focus, and close knowledge gaps.

Abstract:

Tropical cyclones (TCs) are drivers of extreme rainfall and surge, but the current and future TC rainfall–surge joint hazard has not been well quantified. Using a physics-based approach to simulate TC rainfall and storm tides, we show drastic increases in the joint hazard from historical to projected future (SSP5–8.5) conditions. The frequency of joint extreme events (exceeding both hazards’ historical 100-year levels) may increase by 7–36-fold in the southern US and 30–195-fold in the Northeast by 2100. This increase in joint hazard is induced by sea-level rise and TC climatology change; the relative contribution of TC climatology change is higher than that of sea-level rise for 96% of the coast, largely due to rainfall increases. Increasing storm intensity and decreasing translation speed are the main TC change factors that cause higher rainfall and storm tides and up to 25% increase in their dependence.

Press Release

Data Availability:
https://www.nature.com/articles/s41467-022-32018-4#data-availability 

Abstract:

The impacts of climate change are increasingly visible and threaten our wellbeing through complex pathways1,2. One overlooked but critical route is the undermining of the fiscal health of local governments3. There is currently limited understanding of how climate changes may negatively alter the fiscal condition of local governments, and how public officials can best focus their resources to moderate these increasing risks. For local governments to continue to provide essential place-based public services, such as education, police and fire protection, and housing and community development, they need to manage the increasing risks of declines in certain sources of revenues and growing expenditure related to climate hazards, including emergency response, defensive expenditures and increased infrastructure operating costs. In this Comment, we will use the United States as an example to highlight the consequences of climate risks for local government budgets and provide a forward-looking framework to anticipate the fiscal risks of climate change and identify the comprehensive budgetary benefits of adaptation efforts.

Abstract:

Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We first explore correlations between SLR and TC activity by inference from two large‑scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5-8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific. To explore the impact of the joint changes on flood hazard, we then conduct climatologyhydrodynamic modeling with New York City (NYC) as an example. Coastal flood hazard at NYC correlates strongly with global mean surface air temperature (GSAT), due to joint increases in both sea level and TC storm surges, the later driven by stronger and more slowly moving TCs. If positive correlations between SLR and TC changes are ignored in estimating flood hazard, the average projected change to the historical 100 year storm tide event is under-estimated by 0.09 m (7%) and the range across CMIP6 models is underestimated by 0.17 m (11 %). Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT changes will not accurately represent future flood hazard.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EF002462#open-research-section

Additional Code/Data Repository: 

Zenodo:
DOI 10.5281/zenodo.10928067, https://zenodo.org/records/10928068, Creative Commons Attribution 4.0 International
DOI 10.5281/zenodo.10928181, https://zenodo.org/records/10928182

Github code is in a branch from the main repository: https://github.com/Joejoe12341234/LOCKWOOD2022/tree/patch-1

Abstract:

Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience.

Press Release

Data Availability: 

https://www.nature.com/articles/s41467-022-32018-4#data-availability

Abstract:

Coastal areas are subject to the joint risk associated with rainfall-driven flooding and storm surge hazards. To capture this dependency and the compound nature of these hazards, bivariate modelling represents a straightforward and easy-to-implement approach that relies on observational records. Most existing applications focus on a single tide gauge–rain gauge/stream gauge combination, limiting the applicability of bivariate modelling to develop high-resolution space–time design events that can be used to quantify the dynamic, that is, varying in space and time, compound flood hazard in coastal basins. Moreover, there is a need to recognize that not all extreme events always come from a single population, but can reflect a mixture of different generating mechanisms. Therefore, this paper describes an empirical approach to develop design storms with high-resolution in space and time (i.e., ~5 km and hourly) for different joint annual exceedance probabilities. We also stratify extreme rainfall and storm surge events depending on whether they were caused by tropical cyclones (TCs) or not. We find that there are significant differences between the TC and non-TC populations, with very different dependence structures that are missed if we treat all the events as coming from a single population. While we apply this methodology to one basin near Houston, Texas, our approach is general enough to make it applicable for any coastal basin exposed to compounding flood hazards from storm surge and rainfall-induced flooding.

Data Availability: 

https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7825#open-research-section

Abstract:

The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of the influences of diverse processes, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling and observational studies in order to reduce uncertainties in ice sheet projections.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF002772#open-research-section

Abstract:

Barrier islands and their associated backbarrier environments protect mainland population centers and infrastructure from storm impacts, support biodiversity, and provide long-term carbon storage, among other ecosystem services. Despite their socio-economic and ecological importance, the response of coupled barrier-marsh-lagoon environments to sea-level rise is poorly understood. Undeveloped barrier-marsh-lagoon systems typically respond to sea-level rise through the process of landward migration, driven by storm overwash and landward mainland marsh expansion. Such response, however, can be affected by human development and engineering activities such as lagoon dredging and shoreline stabilization. To better understand the difference in the response between developed and undeveloped barrier-marsh-lagoon environments to sea-level rise, we perform a local morphologic analysis that describes the evolution of Long Beach Island (LBI), New Jersey, over the last 182 years. We find that between 1840 and 1934 the LBI system experienced landward migration of all five boundaries, including 171 meters of shoreline retreat. Between the 1920s and 1950s, however, there was a significant shift in system behavior that coincided with the onset of groin construction, which was enhanced by beach nourishment and lagoon dredging practices. From 1934 to 2022 the LBI system experienced ~22 meters of shoreline progradation and a rapid decline in marsh platform extent. Additionally, we extend a morphodynamic model to describe the evolution of the system in terms of five geomorphic boundaries: the ocean shoreline and backbarrier-marsh interface, the seaward and landward lagoon-marsh boundaries, and the landward limit of the inland marsh. We couple this numerical modeling effort with the map analysis during the undeveloped phase of LBI evolution, between 1840 and 1934. Despite its simplicity, the modeling framework can describe the average cross-shore evolution of the barrier-marsh-lagoon system during this period without accounting for human landscape modifications, supporting the premise that natural processes were the key drivers of morphological change. Overall, these results suggest that anthropogenic effects have played a major role in the evolution of LBI over the past century by altering overwash fluxes and marsh-lagoon geometry; this is likely the case for other barrier-marsh-lagoon environments around the world.

Data Availability: 

https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.958573/full#h10

Abstract:

Storm surge barriers, levees, and other coastal flood defense megaprojects are currently being proposed as strategies to protect several US cities against coastal storms and rising sea levels. However, social conflict and other political factors add a layer of complexity that casts doubt on their status as practical climate adaptation options. The specific mechanisms responsible for some projects not progressing beyond initial planning stages remains unclear. In this study, we examined the outcome of two USACE storm surge barrier proposals to explore the political reasons why some coastal flood protection megaprojects break ground in the US, while others do not. Using original archive research, we concluded that storm surge barriers are politically challenging climate adaptation options because of modern environmental laws that provide avenues for expression of oppositional views within the decision process and the allure of alternative options that are more aesthetically pleasing and cheaper and faster to implement. To better allocate public resources and utilize the expertise of USACE, future flood protection megaprojects should first achieve broad support from the public, nongovernmental organizations (NGOs), and elected officials before beginning serious planning. This support could be achieved through new innovative designs that simultaneously address adverse environmental impacts and provide cobenefits (e.g., recreation). New designs should be studied to better understand the level of protection offered and their associated reliability so that USACE has confidence in their use.

Press Release

Abstract:

Accurate delineation of compound flood hazard requires joint simulation of rainfall-runoff and storm surges within high-resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM-OS), which has been widely used for storm surge assessment, and apply it for rainfall-surge compound hazard assessment under climate change at the catchment-scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics-based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM-OS-BQ) to select a small number (∼100) of storms, which are simulated within a high-resolution flood model to characterize the compound flood hazard. We show that the limited JPM-OS-BQ simulations can capture historical flood return levels within 0.25 m compared to a high-fidelity Monte Carlo approach. We find that the combined impact of 2100 sea-level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100-year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM-OS-BQ framework leads to different 100-year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the US Atlantic and Gulf coasts under a variety of climate and environment scenarios.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003097#open-research-section

Abstract:

Hurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical-deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root-mean-square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi-enclosed) regions.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037617#open-research-section

Additional Code/Data Repository: 

Zenodo:
Code: DOI 10.5281/zenodo.7261530, https://zenodo.org/records/7261531
Data: DOI 10.5281/zenodo.10929025, https://zenodo.org/records/10929026

Github: https://github.com/JWLockwood/Neural_network_paper/tree/main

Abstract:

Climate change impacts threaten the stability of the US housing market. In response to growing concerns that increasing costs of flooding are not fully captured in property values, we quantify the magnitude of unpriced flood risk in the housing market by comparing the empirical and economically efficient prices for properties at risk. We find that residential properties exposed to flood risk are overvalued by US$121–US$237 billion, depending on the discount rate. In general, highly overvalued properties are concentrated in counties along the coast with no flood risk disclosure laws and where there is less concern about climate change. Low-income households are at greater risk of losing home equity from price deflation, and municipalities that are heavily reliant on property taxes for revenue are vulnerable to budgetary shortfalls. The consequences of these financial risks will depend on policy choices that influence who bears the costs of climate change.

Press Release

Data Availability:

https://www.nature.com/articles/s41558-023-01594-8#data-availability

The input datasets used for this analysis are either already publicly available or cannot be made available due to restrictive data sharing agreements. The code used for this analysis is available in a Zenodo repository at https://doi.org/10.5281/zenodo.7420416.

Abstract:

Two tropical cyclones (TCs) that make landfall close together can induce sequential hazards to coastal areas. Here we investigate the change in sequential TC hazards in the historical and future projected climates.

We find that the chance of sequential TC hazards has been increasing over the past several decades at many US locations. Under the high (moderate) emission scenario, the chance of hazards from two TCs impacting the same location within 15 days may substantially increase, with the return period decreasing over the century from 10–92 years to ~1–2 (1–3) years along the US East and Gulf coasts, due to sea-level rise and storm climatology change. Climate change can also cause unprecedented compounding of extreme hazards at the regional level. A Katrina-like TC and a Harvey-like TC impacting the United States within 15 days of each other, which is non-existent in the control simulation for over 1,000 years, is projected to have an annual occurrence probability of more than 1% by the end of the century under the high emission scenario.

Press Release

Data Availability:

https://www.nature.com/articles/s41558-023-01595-7#data-availability

Abstract:

Sea-level rise (SLR) amplifies the frequency of extreme sea levels by raising their baseline height. Amplifications are often projected for arbitrary future years and benchmark frequencies. Consequently, such projections do not indicate when flood risk thresholds may be crossed given the current degree of local coastal protection. To better support adaptation planning, we project the timing of the frequency amplification of extreme sea levels relative to estimated local flood protection standards, using SLR projections of IPCC AR6 until 2150. Our central estimates indicate that those degrees of protection will be exceeded 10 times as frequently within the next 30 years (the lead time that large adaptation measures may take) at 26 & 32% of the tide gauges considered, and annually at 4 & 8%, for respectively a low & high emissions scenario. Adaptation planners may use our framework to assess the available lead time and useful lifetime of protective infrastructure.

Press Release

Data Availability:

https://www.nature.com/articles/s41558-023-01616-5#data-availability

Abstract:

While there is evidence for an acceleration in global mean sea level (MSL) since the 1960s, its detection at local levels has been hampered by the considerable influence of natural variability on the rate of MSL change. Here we report a MSL acceleration in tide gauge records along the U.S. Southeast and Gulf coasts that has led to rates (>10 mm yr−1 since 2010) that are unprecedented in at least 120 years. We show that this acceleration is primarily induced by an ocean dynamic signal exceeding the externally forced response from historical climate model simulations. However, when the simulated forced response is removed from observations, the residuals are neither historically unprecedented nor inconsistent with internal variability in simulations. A large fraction of the residuals is consistent with wind driven Rossby waves in the tropical North Atlantic. This indicates that this ongoing acceleration represents the compounding effects of external forcing and internal climate variability.

Press Release

Data Availability:

https://www.nature.com/articles/s41467-023-37649-9#data-availability

Additional Code/Data Repository

Zenodo: https://zenodo.org/records/11105950

Abstract:

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices.

In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs.
We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature.

For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.

Abstract:

Overwash deposits from tropical cyclone-induced storm surges are commonly used as modern analogues for paleo-storm studies. However, the evolution of these deposits between their time of deposition and their incorporation into the geologic record is poorly understood. To understand how the characteristics of an overwash deposit can change over time, we analyzed overwash deposits from four mangrove islands in southern Florida two to three months and twenty-two months after Hurricane Irma's landfall in the region on 10 September 2017. We analyzed the stratigraphy, mean grain size, organic and carbonate contents, stable carbon isotopic signatures, and microfossil (foraminifera and diatom) assemblages of pre-Irma and Irma overwash sediments. Hurricane Irma's storm surge deposited light gray carbonate muds and sands up to 11 cm thick over red organic-rich mangrove peats throughout mangrove islands in southern Florida. Stratigraphy, grain size, loss-on-ignition, and foraminifera analyses provided the strongest evidence for differentiating Irma's overwash deposit from underlying mangrove peats and, if preserved, are expected to identify Hurricane Irma's overwash event within the geologic record. Mean grain size showed the overwash deposit (5.0 ± 0.8 ɸ) was coarser than underlying mangrove peats (6.7 ± 0.7 ɸ), and loss-on-ignition showed the overwash deposit had a lower organic content (19.8 ± 9.1%) and a higher carbonate content (67.8 ± 20.7%) than the underlying peats (59.4 ± 14.6% and 33.7 ± 11.0%, respectively). The overwash deposit was dominated by a diverse, abundant assemblage of sub-tidal benthic calcareous foraminifera compared to a uniform, sparse assemblage of agglutinated foraminifera in the pre-Irma mangrove peats. Geochemical indicators were not able to provide evidence of an overwash event by differentiating organic δ13C or C/N of the overwash deposit from those of the mangrove peats. The complex relationship between diatoms and local environmental factors prevented diatom assemblages from providing a statistically clear distinction between Irma's overwash sediments and underlying mangrove peats. By visiting Hurricane Irma's overwash deposit immediately following landfall and nearly two years post-storm, we were able to document how the overwash deposit's characteristics changed over time. Continued monitoring on the scale of five to ten years would provide further insights into the preservation of overwash deposits for paleo-storm studies.

Data Availability: 

https://www.sciencedirect.com/science/article/pii/S0025322723000890?via%3Dihub#da0005

Abstract:

Low elevation equatorial and tropical coastal regions are highly vulnerable to sea level rise. Here we provide probability perspectives of future sea level for Singapore using regional geological reconstructions and instrumental records since the last glacial maximum ~21.5 thousand years ago. We quantify magnitudes and rates of sea-level change showing deglacial sea level rose from ~121 m below present level and increased at averaged rates up to ~15 mm/yr, which reduced the paleogeographic landscape by ~2.3 million km2. Projections under a moderate emissions scenario show sea level rising 0.95 m at a rate of 7.3 mm/yr by 2150 which has only been exceeded (at least 99% probability) during rapid ice mass loss events ~14.5 and ~9 thousand years ago. Projections under a high emissions scenario incorporating low confidence ice-sheet processes, however, have no precedent during the last deglaciation.

Data Availability: 

https://www.nature.com/articles/s43247-023-00868-5#data-availability

Abstract:

Future sea-level change is characterized by both quantifiable and unquantifiable uncertainties. Effective communication of both types of uncertainties is a key challenge for translating sea-level science to inform long-term coastal planning. Scientific assessments play a key role in the translation process and have taken diverse approaches to communicating sea-level projection uncertainty. Here, we review how past Intergovernmental Panel on Climate Change (IPCC) and regional assessments have presented sea-level projection uncertainty, how IPCC presentations have been interpreted by regional assessments, and how regional assessments and policy guidance simplify projections for practical use. This information influenced the IPCC Sixth Assessment Report (AR6) presentation of quantifiable and unquantifiable uncertainty with the goal of preserving both elements as projections are adapted for regional application.

Plain Language Summary: 

For at least the ~40 years modern scientists have been trying to project future sea-level rise, we’ve been aware that there’s a lot we don’t understand about ice sheets, and that there’s a lot of cutting edge science that needs to be done to understand how ice sheets will respond in a warming climate. Even though there has been a huge amount of progress in scientific understanding, this remains true today. And yet adaptation planners have to plan for sea-level rise taking into account not just ‘well-known unknowns’ whose odds are comparatively straightforward to assess, but ‘poorly known unknowns’ that are hard to quantify. (The latter type of uncertainty is technically called ‘ambiguity’ or ‘deep uncertainty’). It’s not good risk management to simply ignore risks that are hard to measure. And so the challenge for climate scientists who work on assessments intended to inform decision makers has been how to communicate both types of an uncertainty in a manner that neither leads to ignoring the ‘poorly known unknowns’ nor leads to catastrophism that treats high-end outcomes as though they are certainties; the former can put people at risk of harm, the latter can create risks of costs and disruptions that may not be necessary. In this article, we look at the history of communicating ambiguity in assessment of future sea-level rise, with a particular focus on the IPCC, and how these communications impact the users of assessments. We situate the approach adopted by the IPCC’s Sixth Assessment Report in the context of this history, and believe that it will better help users strike an informed balance between ignoring high-end outcomes and being overwhelmed by them.

Press Release

Data Availability: 

https://www.nature.com/articles/s41558-023-01691-8#data-availability

Abstract:

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).

Data Availability: 

https://gmd.copernicus.org/articles/16/4331/2023/#section8

Abstract:

Several coastal ecosystems—most notably mangroves and tidal marshes—exhibit biogenic feedbacks that are facilitating adjustment to relative sea-level rise (RSLR), including the sequestration of carbon and the trapping of mineral sediment1. The stability of reef-top habitats under RSLR is similarly linked to reef-derived sediment accumulation and the vertical accretion of protective coral reefs2. The persistence of these ecosystems under high rates of RSLR is contested3. Here we show that the probability of vertical adjustment to RSLR inferred from palaeo-stratigraphic observations aligns with contemporary in situ survey measurements. A deficit between tidal marsh and mangrove adjustment and RSLR is likely at 4 mm yr−1 and highly likely at 7 mm yr−1 of RSLR. As rates of RSLR exceed 7 mm yr−1, the probability that reef islands destabilize through increased shoreline erosion and wave overtopping increases. Increased global warming from 1.5 °C to 2.0 °C would double the area of mapped tidal marsh exposed to 4 mm yr−1 of RSLR by between 2080 and 2100. With 3 °C of warming, nearly all the world’s mangrove forests and coral reef islands and almost 40% of mapped tidal marshes are estimated to be exposed to RSLR of at least 7 mm yr−1. Meeting the Paris agreement targets would minimize disruption to coastal ecosystems.

Press Release

Data Availability: 

https://www.nature.com/articles/s41586-023-06448-z#data-availability

Abstract:

There are contrasting views on how to produce the accurate predictions that are needed to guide climate change adaptation. Here, we argue for harnessing artificial intelligence, building on domain-specific knowledge and generating ensembles of moderately high-resolution (10–50 km) climate simulations as anchors for detailed hazard models.

Abstract:

Mapping inundation areas and flood depths is necessary for coastal and riverine management and planning. Flood maps help communicate flooding risk to affected communities and vulnerable populations and are essential for evaluating flooding impacts. Here, we introduce MatFlood, a computationally efficient static flood tool that exploits image-processing algorithm for estimation of flood extension and depth. Features include (a) an algorithm that evaluates hydro-connectivity; (b) functionality to calculate spatially varying flood water levels and (c) the inclusion of a reduction factor to mimic the effects of physical processes not explicitly resolved. The efficiency of the tool is well-suited for simulating numerous flooding maps using different inputs (flood water levels or digital elevation models), over large areas, and high spatial resolution. We apply MatFlood to assess the flood extent and depth of Hurricane Sandy (2012) in the New York/New Jersey area to illustrate its use. In comparison to existing approaches based on geographic information systems, MatFlood performs the same calculations six times faster in the Hurricane Sandy study case.

Data Availability: 

https://www.sciencedirect.com/science/article/pii/S1364815223002153?via%3Dihub#da0010

Abstract:

Small Island Developing States (SIDS) have long been recognized as some of the planet’s most vulnerable areas to climate change, notably to rising sea levels and coastal extremes. They have been crucial in raising ambitions to keep global warming below 1.5 °C and in advancing the difficult debate on loss and damage. Still, quantitative estimates of loss and damage for SIDS under different mitigation targets are lacking. Here we carry out an assessment of future flood risk from slow-onset sea-level rise and episodic sea-level extremes along the coastlines of SIDS worldwide. We show that by the end of this century, without adaptation, climate change would amplify present direct economic damages from coastal flooding by more than 14 times under high-emissions scenarios. Keeping global warming below 1.5 °C could avoid almost half of unmitigated damage, depending on the region. Achieving this climate target, however, would still not prevent several SIDS from suffering economic losses that correspond to considerable shares of their GDP, probably leading to forced migration from low-lying coastal zones. Our results underline that investments in adaptation and sustainable development in SIDS are urgently needed, as well as dedicated support to assisting developing countries in responding to loss and damage due to climate change.

Data Availability: 

https://www.nature.com/articles/s41893-023-01230-5#data-availability

Abstract:

The U.S. coastlines have experienced rapid increases in occurrences of High Tide Flooding (HTF) during recent decades. While it is generally accepted that relative mean sea level (RMSL) rise is the dominant cause for this, an attribution to individual components is still lacking. Here, we use local sea-level budgets to attribute past changes in HTF days to RMSL and its individual contributions. We find that while RMSL rise generally explains > 84% of long-term increases in HTF days locally, spatial patterns in HTF changes also depend on differences in flooding thresholds and water level characteristics. Vertical land motion dominates long-term increases in HTF, particularly in the northeast, while sterodynamic sea level (SDSL) is most important elsewhere and on shorter temporal scales. We also show that the recent SDSL acceleration in the Gulf of Mexico has led to an increase of 220% in the frequency of HTF events over the last decade.

Data Availability: 

https://www.nature.com/articles/s41612-023-00538-5#data-availability

Additional Code/Data Repository:

Github: https://github.com/qsun-SL/Causes-of-accelerated-High-Tide-Flooding-in-the-US-since-1950.git

Abstract:

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.

Data Availability: 

https://gmd.copernicus.org/articles/16/7461/2023/#section7

Additional Code/Data Repository:

Github: https://github.com/radical-collaboration/facts

Abstract:

Climate change is expected to intensify the effects of extreme weather events on power systems and increase the frequency of severe power outages. The large-scale integration of environment-dependent renewables during energy decarbonization could induce increased uncertainty in the supply–demand balance and climate vulnerability of power grids. This Perspective discusses the superimposed risks of climate change, extreme weather events and renewable energy integration, which collectively affect power system resilience. Insights drawn from large-scale spatiotemporal data on historical US power outages induced by tropical cyclones illustrate the vital role of grid inertia and system flexibility in maintaining the balance between supply and demand, thereby preventing catastrophic cascading failures. Alarmingly, the future projections under diverse emission pathways signal that climate hazards — especially tropical cyclones and heatwaves — are intensifying and can cause even greater impacts on the power grids. High-penetration renewable power systems under climate change may face escalating challenges, including more severe infrastructure damage, lower grid inertia and flexibility, and longer post-event recovery. Towards a net-zero future, this Perspective then explores approaches for harnessing the inherent potential of distributed renewables for climate resilience through forming microgrids, aligned with holistic technical solutions such as grid-forming inverters, distributed energy storage, cross-sector interoperability, distributed optimization and climate–energy integrated modelling.

Figures 2 and 3 Code:

Github: https://github.com/LuoXu-THU/Resilience-of-renewable-power-systems-under-climate-risks

Abstract:

Sediment cores from blue holes have emerged as a promising tool for extending the record of long-term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological-hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias-correct the climatological-hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500-year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias-corrected climatological-hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500-year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2-4.5 and SSP5-8.5, respectively by the end of the 21st century.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020354#open-research-section

Additional Code/Data Repository: 

Zenodo: DOI 10.5281/zenodo.10957435, https://zenodo.org/records/10957435

Github: https://github.com/ab3838/Integrating-Climatological-Hydrodynamic-Modeling.git

Abstract:

Sediment cores from blue holes have emerged as a promising tool for extending the record of long-term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological-hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias-correct the climatological-hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500-year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias-corrected climatological-hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500-year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2-4.5 and SSP5-8.5, respectively by the end of the 21st century.

Data Availability: 

https://www.sciencedirect.com/science/article/pii/S1474034624000752?via%3Dihub#da005

Additional Code/Data Repository: 

Github: https://github.com/sunhoro/HIAV

Abstract:

Tropical cyclones (TCs) that undergo rapid intensification (RI) before landfall are notoriously difficult to predict and have caused tremendous damage to coastal regions in the United States. Using downscaled synthetic TCs and physics-based models for storm tide and rain, we investigate the hazards posed by TCs that rapidly intensify before landfall under both historical and future mid-emissions climate scenarios. In the downscaled synthetic data, the percentage of TCs experiencing RI is estimated to rise across a significant portion of the North Atlantic basin. Notably, future climate warming causes large increases in the probability of RI within 24 hr of landfall. Also, our analysis shows that RI events induce notably higher rainfall hazard levels than non-RI events with equivalent TC intensities. As a result, RI events dominate increases in 100-year rainfall and storm tide levels under climate change for most of the US coastline.

Data Availability: 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL105624#open-research-section

Additional Code/Data Repository: 

Zenodo: DOI 10.5281/zenodo.10928315, https://zenodo.org/records/10928316

Github: https://github.com/Joejoe12341234/Lockwood_2024_RapidInt

Abstract:

Accurate flood forecasting and efficient emergency response operations are vital, especially in the case of urban flash floods. The dense distribution of power lines in urban areas significantly impacts search and rescue operations during extreme flood events. However, no existing emergency response frameworks have incorporated the impacts of overhead power lines on lifeboat rescue operations. This study aims to determine the necessity and feasibility of incorporating overhead power line information into an emergency response framework using Manville, New Jersey during Hurricane Ida as a test bed. We propose an integrated framework, which includes a building-scale flood model, urban point cloud data, a human vulnerability model, and network analysis, to simulate rescue operation feasibility during Hurricane Ida. Results reveal that during the most severe point of the flood event, 46% of impacted buildings became nonrescuable due to complete isolation from the road network, and a significant 67.7% of the municipality’s areas that became dangerous for pedestrians also became inaccessible to rescue boats due to overhead power line obstruction. Additionally, we identify a continuous 10-hour period during which an average of 43.4% of the 991 impacted buildings faced complete isolation. For these structures, early evacuation emerges as the sole means to prevent isolation. This research highlights the pressing need to consider overhead power lines in emergency response planning to ensure more effective and targeted flood resilience measures for urban areas facing increasingly frequent extreme precipitation events.

Data Availability: 

https://www.sciencedirect.com/science/article/pii/S2212420924001213?via%3Dihub#da1

Additional Code/Data Repository: 

Github: https://github.com/ywang910516/Overhead-Powerline-Project

Abstract:

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments

Data Availability: 

https://www.sciencedirect.com/science/article/pii/S2212094724000239?via%3Dihub#da0010

Abstract:

Decision-makers increasingly invoke equity to motivate, design, implement and evaluate strategies for managing flood risks. Unfortunately, there is little guidance on how analysts can develop measurements that support these tasks. Here we analyse how equity can be defined and measured by surveying 167 peer-reviewed publications that explicitly state an interest in equity in the context of flood-risk management. Our main result is a taxonomy that systematizes how equity has been, and can be, defined and measured in flood-risk research. The taxonomy embodies how equity is a pluralistic and unavoidably ethical concept. Despite this, we find that most quantitative studies fail to motivate or defend critical value judgements on which their findings depend. We also find that studies often include only a single equity measurement. This practice can overlook important trade-offs between competing perspectives on equity. For example, the few studies that employ distinct principles show that conclusions about equity depend on which principle underlies a specific measurement and how that principle is operationalized. We draw on our analysis to suggest practices for developing more useful equity indicators and performing more comprehensive quantitative equity assessments in the broader context of environmental risks.

Plain Language Summary: 

Decision-makers around the world are working to establish equity as a foundation of public policy. This is increasingly true in the case of flood-risk management. Equity measurements are needed to help track progress towards stating and meeting equity goals. However, a major challenge in measuring equity is that when people hold different values about what equity means, they may reach different conclusions about which outcomes are equitable. This study identifies properties of useful equity measurements based on an analysis about how equity is, and can be, measured in flood-risk settings. The results are synthesized in an accessible taxonomy that can help analysts and practitioners develop more useful equity measurements for flood-risk management, and potentially, in other environmental management settings.

Press Release

Data Availability: 

https://www.nature.com/articles/s41893-024-01345-3#data-availability

Abstract:

Geologic reconstructions of overwash events can extend storm records beyond the brief instrumental record. However, the return periods of storms calculated from geologic records alone may underestimate the frequency of events given the preservation bias of geologic records. Here, we compare a geologic reconstruction of storm activity from a salt marsh in New Jersey to two neighboring instrumental records at the Sandy Hook and Battery tide gauges. Eight overwash deposits were identified within the marsh's stratigraphy by their fan‐shaped morphology and coarsermean grain size (3.6±0.7φ) compared to autochthonous sediments they were embedded in (5.6±0.8φ). We used anage–depth model based on modern chronohorizons and three radiocarbon dates to provide age constraints for theoverwash deposits. Seven of the overwash deposits were attributed to historical storms, including the youngest overwash deposit from Hurricane Sandy in 2012. The four youngest overwash deposits overlap with instrumental records. In contrast, the Sandy Hook and Battery tide gauges recorded eight and 11 extreme water levels above the 10%annual expected probability (AEP) of exceedance level, respectively, between 1932/1920 and the present. The geologic record in northern New Jersey, therefore, has a 36–50% preservation potential of capturing extreme water levels abovethe 10% AEP level.

Plain Language Summary: 

Geologic reconstructions can extend storm records beyond the brief instrumental record. However, the return periods of storms calculated from geologic records alone may underestimate the frequency of events given the preservation bias of geologic records. Here, we compare a geologic reconstruction of storm activity from a salt marsh in Cheesequake, New Jersey, to two neighboring instrumental records at the Sandy Hook and Battery tide gauges. We find that the geologic record in northern New Jersey has a 36–50% chance of recording extreme water levels above the 1-in-10 year return period level.

Data Availability: 

https://onlinelibrary.wiley.com/doi/10.1002/jqs.3622#open-research-section

Abstract:

As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization.

Data Availability: 

Training Data Set: https://ucmweb.rutgers.edu/magazine/1419archive/insights/damage-control.html

Abstract:

North Atlantic tropical cyclone (TC) activity under a high-emission scenario is projected using a statistical synthetic storm model coupled with nine Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The ensemble projection shows that the annual frequency of TCs generated in the basin will decrease from 15.91 (1979-2014) to 12.16 (2075-2100), and TC activity will shift poleward and coast-ward. The mean of lifetime maximum intensity will increase from 66.50 knots to 75.04 knots. Large discrepancies in TC frequency and intensity projections are found among the nine CMIP6 climate models. The uncertainty in the projection of wind shear is the leading cause of the discrepancies in the TC climatology projection, dominating the uncertainties in the projection of thermodynamic parameters such as potential intensity and saturation deficit. The uncertainty in the projection of wind shear may be related to the different projections of horizontal gradient of vertically integrated temperature in the climate models, which can be induced by different parameterizations of physical processes including surface process, sea ice, and cloud feedback. Informed by the uncertainty analysis, a surrogate model is developed to provide the first-order estimation of TC activity in climate models based on large-scale environmental features.

Data Availability: 

CMIP6 simulation outputs: https://aims2.llnl.gov/search/cmip6/

Abstract:

The Intergovernmental Panel on Climate Change (IPCC) exists to provide policy-relevant assessments of the science related to climate change. As such, the IPCC has long grappled with characterizing and communicating uncertainty in its assessments. Decision Making under Deep Uncertainty (DMDU) is a set of concepts, methods, and tools to inform decisions when there exist substantial and significant limitations on what is and can be known about policy-relevant questions. Over the last twenty-five years, the IPCC has drawn increasingly on DMDU concepts to more effectively include policy-relevant, but lower-confidence scientific information in its assessments. This paper traces the history of the IPCC’s use of DMDU and explains the intersection with key IPCC concepts such as risk, scenarios, treatment of uncertainty, storylines and high-impact, low-likelihood outcomes, and both adaptation and climate resilient development pathways. The paper suggests how the IPCC might benefit from enhanced use of DMDU in its current (7th) assessment cycle.

Data Availability: 

https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2024.1380054/full#h6

Abstract:

As the global impact of climate change intensifies, there is an urgent need for equitable and efficient climate adaptation policies. Traditional approaches for allocating public resources for climate adaptation that are based on economic benefit-cost analysis often overlook the resulting distributional inequalities. In this study, we apply equity weightings to mitigate the distributional inequalities in two key building and household level adaptation strategies under changing coastal flood hazards: property buyouts and building retrofit in New York City (NYC). Under a mid-range emissions scenario, we find that unweighted benefit cost ratios applied to residential buildings are higher for richer and non-disadvantaged census tracts in NYC. The integration of income-based equity weights alters this correlation effect, which has the potential to shift investment in mitigation towards poorer and disadvantaged census tracts. This alteration is sensitive to the value of elasticity of marginal utility, the key parameter used to calculate the equity weight. Higher values of elasticity of marginal utility increase benefits for disadvantaged communities but reduce the overall economic benefits from investments, highlighting the trade-offs in incorporating equity into adaptation planning.

Data Availability: 

https://iopscience.iop.org/article/10.1088/1748-9326/ad4ef8#erlad4ef8s5

Abstract:

Compound flooding, the concurrence of multiple flooding mechanisms such as storm surge, heavy rainfall, and riverine flooding, poses a significant threat to coastal communities. To mitigate the impacts of compound flooding, forecasts must represent the variability of flooding drivers over a wide range of spatial scales while remaining timely. One approach to develop these forecasts is through subgrid corrections, which utilize information at smaller scales to “correct” water levels and current velocities averaged over the model scale. Recent studies have shown that subgrid models can improve both accuracy and efficiency; however, existing models are not able to account for the dynamic interactions of hydrologic and hydrodynamic drivers and their contributions to flooding along the smallest flow pathways when using a coarse resolution. Here, we have developed a solver called CoaSToRM (Coastal Subgrid Topography Research Model) with subgrid corrections to compute compound flooding in coastal systems resulting from fluvial, pluvial, tidal, and wind-driven processes. A key contribution is the model’s ability to enforce all flood drivers and use the subgrid corrections to improve the accuracy of the coarse-resolution simulation. The model is validated for Hurricane Eta 2020 in Tampa Bay, showing improved prediction accuracy with subgrid corrections at 42 locations. Subgrid models with coarse resolutions (R2 = 0.70, 0.73, 0.77 for 3-, 1.5-, 0.75-km grids) outperform standard counterparts (R2 = 0.03, 0.14, 0.26). A 3-km subgrid simulation runs roughly 50 times faster than a 0.75-km subgrid simulation, with similar accuracy.

Data Availability: 

https://github.com/ab3838/CoaSToRM

Abstract:

Tropical cyclone (TC) winds control design wind speeds for much of the eastern United States. Those winds are likely to intensify with climate change, but climate change was not considered in the ASCE 7-22 design wind speed maps, potentially causing many structures to be designed with unacceptably high levels of risk. In this study, we investigate (1) the increases in design wind speed due to climate change; and (2) the resulting risk to structures if climate change is not considered. We estimated the design wind speeds for US counties affected by TCs along the Gulf and Atlantic coasts using nonstationary methods based on a set of synthetic TCs (1,000–1,500 year simulations) downscaled from the latest global climate projections (CMIP6) for the high-emissions scenario (SSP5-8.5). It was found that over the 21st century, 50-year return period winds would increase by an average of around 10% along the US Gulf and Atlantic coasts. Depending on the risk category, design lifetime, and year of construction, design wind speeds (targeting lifetime exceedance probability) are projected to increase by an average of 3%–6% for all counties studied and 6%–15% for coastal counties. For Risk Category II–IV structures, depending on the design lifetime and year of construction, 8%–36% of all counties studied and 25%–66% of coastal counties would experience projected lifetime exceedance probabilities that were at least two risk categories too low; for example, in up to 26% of all counties studied and 54% of coastal counties, a Risk Category III structure would be effectively designed as Risk Category I or lower.

Data Availability: 

https://ascelibrary.org/doi/10.1061/JSENDH.STENG-11899#sec-8

Abstract:

Climate risks are growing. Research is increasingly important to inform the design of risk-management strategies. Assessing such strategies necessarily brings values into research. But the values assumed within research (often only implicitly) may not align with those of stakeholders and decision makers. These misalignments are often invisible to researchers and can severely limit research relevance or lead to inappropriate policy advice. Aligning strategy assessments with stakeholders' values requires a holistic approach to research design that is oriented around those values from the start. Integrating values into research in this way requires collaboration with stakeholders, integration across disciplines, and attention to all aspects of research design. Here we describe and demonstrate a qualitative conceptual tool called a values-informed mental model (ViMM) to support such values-centered research design. ViMMs map stakeholders' values onto a conceptual model of a study system to visualize the intersection of those values with coupled natural-human system dynamics. Through this mapping, ViMMs integrate inputs from diverse collaborators to support the design of research that assesses risk-management strategies in light of stakeholders' values. We define a visual language for ViMMs, describe accompanying practices and workflows, and present an illustrative application to the case of flood-risk management in a small community along the Susquehanna river in the Northeast United States.

Abstract:

As a vulnerable population, immigrants can be disproportionately affected by disasters. Because of their legal and migratory status, immigrants may have different challenges, needs, and possibilities when facing a disaster. Yet, within disaster studies, immigrants are rarely studied alone. Instead, they are often considered part of the large heterogeneous group of racial and ethnic minorities in the United States. This racial classification points to a gap in the literature and in our understanding of how disadvantaged groups may cope with disasters. To address this gap, the current study hypothesizes that: (1) Immigrants have unique experiences and disaster impacts compared to the broader aggregated category of racial and ethnic minorities in the U.S. and (2) There are variations in disaster experiences and impacts across different types of immigrant subgroups beyond refugees. To explore these hypotheses, a study of the literature across six databases from 2018 to 2023was conducted. The review identified a total of 17 articles discussing immigrant experiences during disasters. Major cross-cutting themes on immigrant disaster experiences include fear of deportation, restrictive immigration status, excessive economic burden and labor exploitation, employment rigidity, adverse health outcomes, limited informational resources and limited social capital, selective disaster relief measures, and infrastructural challenges as regards to housing and transportation. Many of the themes identified are unique to immigrants, such as the fear of deportation, restrictive immigration status and visa policies, and selective disaster relief measures.

Abstract:

Measuring and managing the risk of extensive distribution network outages during extreme events is critical for ensuring system-level energy balance in transmission network operations. However, existing risk measures used in stochastic optimization of power systems are computationally intractable for this problem involving large numbers of discrete random variables. Using a new coherent risk measure, Entropic Value-at-Risk (EVaR), that requires significantly less computational complexity, we propose an EVaR-constrained optimal power flow model that can quantify and manage the outage risk of extensive distribution feeders. The optimization problem with EVaR constraints on discrete random variables is equivalently reformulated as a conic programming model, which allows the problem to leverage the computational efficiency of conic solvers. The superiority of the proposed model is validated on the real-world Puerto Rico transmission grid combined with its large-scale distribution networks.