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Relative sea level rise (RSLR) has driven large increases in annual water level exceedances (duration and frequency) above minor (nuisance level) coastal flooding elevation thresholds established by the National Weather Service (NWS) at U.S. tide gauges over the last half‐century. For threshold levels below 0.5 m above high tide, the rates of annual exceedances are accelerating along the U.S. East and Gulf Coasts, primarily from evolution of tidal water level distributions to higher elevations impinging on the flood threshold. These accelerations are quantified in terms of the local RSLR rate and tidal range through multiple regression analysis. Along the U.S. West Coast, annual exceedance rates are linearly increasing, complicated by sharp punctuations in RSLR anomalies during El Niño Southern Oscillation (ENSO) phases, and we account for annual exceedance variability along the U.S. West and East Coasts from ENSO forcing. Projections of annual exceedances above local NWS nuisance levels at U.S. tide gauges are estimated by shifting probability estimates of daily maximum water levels over a contemporary 5‐year period following probabilistic RSLR projections of Kopp et al. (2014) for representative concentration pathways (RCP) 2.6, 4.5, and 8.5. We suggest a tipping point for coastal inundation (30 days/per year with a threshold exceedance) based on the evolution of exceedance probabilities. Under forcing associated with the local‐median projections of RSLR, the majority of locations surpass the tipping point over the next several decades regardless of specific RCP. Key Points Nuisance coastal flooding is increasing along U.S. coastlines Event rates accelerate as water level distributions exceed elevation thresholds Tipping points for coastal inundation are surpassed in the coming decades

Support was provided from the US Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) under Project RC‐2644. Jayantha Obeysekera had support from the Institute of Environment, FIU.

The cooccurrence of coastal and riverine flooding leads to compound events with substantial impacts on people and property in low‐lying coastal areas. Climate change could increase the level of compound flood hazard through higher extreme sea levels and river flows. Here, a bivariate flood hazard assessment method is proposed to estimate compound coastal‐riverine frequency under current and future climate conditions. A copula‐based approach is used to estimate the joint return period (JRP) of compound floods by incorporating sea‐level rise (SLR) and changes in peak river flows into the marginal distributions of flood drivers. Specifically, the changes in JRP of compound major coastal‐riverine flooding defined based on simultaneous exceedances above major coastal and riverine thresholds, are explored by midcentury. Subsequently, the increase in the probability of occurrence of at least one compound major coastal‐riverine flooding for a given period of time is quantified. The proposed compound flood hazard assessment is conducted at 26 paired tidal‐riverine stations along the Contiguous United States coast with long‐term data and defined flood thresholds. We show that the northeast Atlantic and the western part of the Gulf coasts are experiencing the highest compound major coastal‐riverine flood probability under current conditions. However, future SLR scenarios show the highest frequency amplification along the southeast Atlantic coast. The impact of changes in peak river flows is found to be considerably less than that of SLR. Climate change impacts, especially SLR, may lead to more frequent compound events, which cannot be ignored for future adaptation responses in estuary regions. Plain Language Summary Compound coastal and inland flooding poses serious threats to many coastal cities around the world. Climate change could increase the level of compound flood hazard through sea level rise (SLR) and higher extreme precipitation and river flows. An example of recent compound flooding in the US is the cooccurrence of heavy precipitation and storm surge from Hurricane Harvey, which caused huge flooding along the Gulf coast of Texas. We propose a compound flood hazard assessment method to project compound coastal‐riverine flood frequency under climate change. Coastal and riverine flood impact thresholds are used to define compound major coastal‐riverine flooding. The application of the method along the Contiguous United States coast shows that SLR is the most prominent factor in increasing the frequency of future major compound flood events and the impact of changes in peak river flows is negligible in most of the locations. Under current climate conditions, the northeast Atlantic and the western part of the Gulf coasts are experiencing the highest compound major coastal‐riverine flood probability. However, future SLR scenarios show emerging high compound major flooding probability along the southeast Atlantic coast. Key Points A bivariate flood hazard assessment method is proposed to estimate compound coastal‐riverine frequency under climate change impacts Future sea level rise scenarios show emerging high compound major flooding probability along the southeast Atlantic coast The impact of changes in peak river flows is found to be significantly less than the impacts of sea level rise

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States’ coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds. Using machine learning algorithms, this study estimates sea level rise and high tide flooding thresholds every 10 km along the United States’ coasts, complementing conventional linear-/point-based estimates and offering insights for ungauged areas.

Sea‐level rise sits at the frontier of usable climate climate change research, because it involves natural and human systems with long lags, irreversible losses, and deep uncertainty. For example, many of the measures to adapt to sea‐level rise involve infrastructure and land‐use decisions, which can have multigenerational lifetimes and will further influence responses in both natural and human systems. Thus, sea‐level science has increasingly grappled with the implications of (1) deep uncertainty in future climate system projections, particularly of human emissions and ice sheet dynamics; (2) the overlay of slow trends and high‐frequency variability (e.g., tides and storms) that give rise to many of the most relevant impacts; (3) the effects of changing sea level on the physical exposure and vulnerability of ecological and socioeconomic systems; and (4) the challenges of engaging stakeholder communities with the scientific process in a way that genuinely increases the utility of the science for adaptation decision making. Much fundamental climate system research remains to be done, but many of the most critical issues sit at the intersection of natural sciences, social sciences, engineering, decision science, and political economy. Addressing these issues demands a better understanding of the coupled interactions of mean and extreme sea levels, coastal geomorphology, economics, and migration; decision‐first approaches that identify and focus research upon those scientific uncertainties most relevant to concrete adaptation choices; and a political economy that allows usable science to become used science. Plain Language Summary The impacts of sea‐level rise pose growing threats to coastal communities, economies, and ecosystems, and decisions made today—in areas like land‐use policies, coastal development, and infrastructure investment—will affect exposure and vulnerability for generations to come. Thus, the usability of sea‐level science is a pressing concern. Ensuring usability requires grappling with deep uncertainty in long‐term sea‐level projections, the relationship between long‐term trends and the impacts of short‐lived extreme events, and the ways in which the physical coast, as well as people and ecosystems along the coast, respond to increasingly frequent flooding. At the same time, it also requires more extensive and deliberate stakeholder engagement throughout the scientific process, as well as cognizance of the political economy of linking stakeholder‐engaged science to action. Key Points Understanding coastal evolution requires accounting for interactions of sea‐level change, geomorphology, socioeconomics, and human responses Deep uncertainty in sea‐level rise projections and impacts exists on timescales relevant to infrastructure and planning decisions Adaptation under deep uncertainty requires co‐production, iterative risk management, and awareness of political economy

High Tide Flooding (HTF) is a present and increasing hazard for coastal communities across the United States. NOAA provides HTF outlooks at U.S. tide gauges, however, many coastal communities lie relatively far from a tide gauge and therefore currently lack localized HTF guidance. In this study, we demonstrate an approach to generate spatially-continuous daily predictions of HTF at 400–500 m resolution out to a year into the future, by combining NOAA’s monthly HTF outlook framework with the newly-released Coastal Ocean Reanalysis (CORA). Using CORA to derive daily HTF predictions at tide gauges, as compared to using gauge observations, results in average HTF model skill reduction of ≤5% using three different statistical metrics at one month lead time. Further, stations which obtain statistically skillful HTF predictions using gauge data also do so using CORA for 94% of cases. The results suggest that CORA could enable skillful HTF predictions away from tide gauges, supporting the possibility of providing high resolution HTF outlooks for much of the U.S. coastline. The potential value of these spatially continuous HTF predictions is illustrated by identifying communities near Charleston S.C. with different CORA-derived local HTF risk than that provided by the closest tide gauge. Finally, we describe outstanding questions and needs for the scaling of these results to an operational national-scale monthly HTF outlook.

Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global extreme sea level (ESL) exceedance probabilities using a regional frequency analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (∼1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline (excluding Antarctica). The methodology presented in this paper is an extension of the regional framework of Sweet et al. (2022), with innovations introduced to incorporate wave setup and apply the method globally. Water level records from tide gauges and a global reanalysis of tide and surge levels are integrated with a global ocean wave reanalysis. Subsequently, these data are regionalised, normalised, and aggregated and then fit with a generalised Pareto distribution. The regional distributions are downscaled to the local scale using the tidal range at every location along the global coastline obtained from a global tide model. The results show 8 cm of positive bias at the 1-in-10-year return level when compared to individual tide gauges. The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. These spatially distributed data not only retain the volume of information but also address the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can improve the characterisation of ESLs in regions prone to tropical cyclone activity. In conclusion, this study provides a valuable resource for quantifying the global coastal flood risk, offering an innovative global methodology that can contribute to preparing for – and mitigating against – coastal flooding.

A regional frequency analysis (RFA) of tide gauge (TG) data fit with a Generalized Pareto Distribution (GPD) is used to estimate contemporary extreme sea level (ESL) probabilities and the risk of a damaging flood along Pacific Basin coastlines. Methods to localize and spatially granulate the regional ESL (sub-annual to 500-yr) probabilities and their uncertainties are presented to help planners of often-remote Pacific Basin communities assess (ocean) flood risk of various threshold severities under current and future sea levels. Downscaling methods include use of local TG observations of various record lengths (e.g., 1-19+ years), and if no in-situ data exist, tide range information. Low-probability RFA ESLs localized at TG locations are higher than other recent assessments and generally more precise (narrower confidence intervals). This is due to increased rare-event sampling as measured by numerous TGs regionally. For example, the 100-yr ESLs (1% annual chance event) are 0.15 m and 0.25 higher (median at-site difference) than a single-TG based analysis that is closely aligned to those supporting recent Intergovernmental Panel on Climate Change (IPCC) assessments and a third-generation global tide and surge model, respectively. Height thresholds for damaging flood levels along Pacific Basin coastlines are proposed. These floods vary between about 0.6-1.2 m or more above the average highest tide and are associated with warning levels of the U.S. National Oceanic and Atmospheric Administration (NOAA). The risk of a damaging flood assessed by the RFA ESL probabilities under contemporary sea levels have about a (median) 20 to 25-yr return interval (4-5% annual chance) for TG locations along Pacific coastlines. Considering localized sea level rise projections of the IPCC associated with a global rise of about 0.5 m by 2100 under a reduced emissions scenario, damaging floods are projected to occur annually by 2055 and >10 times/year by 2100 at the majority of TG locations.