2 Sea-level rise (SLR) poses a range of threats to natural and built environments 1,2 , making assessments of SLR-induced hazards essential for informed decision-making 3 . We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30 m resolution predictions for more than 38,000 km 2 of diverse coastal landscape in the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge.This approach is well-suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints. As an alternative, we developed a data-driven coastal response (CR) model that considers both inundation and dynamic response using a range of SLR scenarios and datasets describing elevation and vertical land movement. We integrate these elements with land-cover information to assess CR likelihoods in the form of a dynamic probability, DP = 1-Prob. (inundate), using a Bayesian network Maine through Virginia, and includes a region with a wide range of coastal development, infrastructure, and environments found globally; including uplands, barrier beaches, spits, islands, mainland beaches, cliffs, rocky headlands, estuaries, and wetlands. The study area is defined by the -10 and +10 m elevation contours and mapped as a 30 m grid.To predict CR likelihoods (Figure 2), we first compute an adjusted land elevation with respect to projected sea levels:where AE represents the adjusted elevation with respect to a future sea level; E denotes the initial land elevation; SL is a projected sea level in the 2020s, 2030s, 2050s, or 2080s; and VLM gives the current rate of vertical land movement due to glacial isostatic adjustment, tectonics, and other non-climatic effects such as groundwater withdrawal and sediment compaction 15 . Sources of uncertainty in AE predictions include SLR projections, elevation data accuracy, vertical datum adjustments, and the interpolation of VLM rates from point data; these geospatially-explicit input uncertainties are propagated through the model to produce a probability mass function P(AE) for every grid cell (Figure 2c,d). Once generated, AEs are related through evaluation of their dynamic response potential with generalized landcover information and used to produce a CR likelihood (Figures 1, 2).Discretized AE predictions provide an estimated submergence level comparable to many existing inundation models 3, 16 (Figure 2). However, our predictions include several notable improvement...

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

13-Sea-storm time series are simulated with a multivariate probabilistic model 14 -Erosion and flooding risk are assessed accurately with a joint probability approach 15 -Return water levels and impact hours could be larger than recently observed 16 Abstract 21We assess erosion and flooding risk in the northern Gulf of Mexico by identifying 22 interdependencies among oceanographic drivers and probabilistically modeling the resulting 23 potential for coastal change. Wave and water level observations are used to determine 24 relationships between six hydrodynamic parameters that influence total water level and 25 therefore erosion and flooding, through consideration of a wide range of univariate 26 distribution functions and multivariate elliptical copulas. Using these relationships, we 27 explore how different our interpretation of the present-day erosion/flooding risk could be if 28 we had seen more or fewer extreme realizations of individual and combinations of parameters 29 in the past by simulating 10,000 physically and statistically consistent sea-storm time series. 30 We find that seasonal total water levels associated with the 100-year return period could be up 31 to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the 32 observational records. Impact hours of collision and overwash -where total water levels 33 exceed the dune toe or dune crest elevations -could be on average 70% (collision) and 100% 34 (overwash) larger than inferred from the observations. Our model accounts for non-35 stationarity in a straightforward, non-parametric way that can be applied (with little 36 adjustments) to many other coastlines. The probabilistic model presented here, which 37 accounts for observational uncertainty, can be applied to other coastlines where short record 38 lengths limit the ability to identify the full range of possible wave and water level conditions 39 that coastal mangers and planners must consider to develop sustainable management 40 strategies. 41 Key words 42Multivariate sea-storm model; elliptical copulas; coastal erosion and flooding; northern Gulf 43 of Mexico 44 3 Introduction 45Erosion and flooding occur on sandy coastlines when the total water level (TWL) exceeds 46 critical thresholds of backshore features. Ruggiero [2013] defined TWL as the superposition 47 of astronomical tide (η A ), storm surge (or non-tidal residual; η NTR ), and the extreme wave 48 runup statistic (R2%; e.g., Stockdon et al. [2014]), all of which can be derived with various 49 numerical or empirical models. The impacts of extreme oceanographic events, in terms of 50 erosion of barrier islands and sandy beaches and flood damages in low-lying coastal areas, 51 also strongly depend on how long critical TWL thresholds are exceeded (i.e. the event 52duration is important). For long-term simulations of erosion, the duration of calm periods 53 between successive sea-storm events are also relevant since they determine how much the 54 beach or dune can recover before the next extreme eve...

This study examines the integrated influence of sea level rise (SLR) and future morphology on tidal hydrodynamics along the Northern Gulf of Mexico (NGOM) coast including seven embayments and three ecologically and economically significant estuaries. A large-domain hydrodynamic model was used to simulate astronomic tides for present and future conditions (circa 2050 and 2100). Future conditions were simulated by imposing four SLR scenarios to alter hydrodynamic boundary conditions and updating shoreline position and dune heights using a probabilistic model that is coupled to SLR. Under the highest SLR scenario, tidal amplitudes within the bays increased as much as 67% (10.0 cm) because of increases in the inlet cross-sectional area. Changes in harmonic constituent phases indicated that tidal propagation was faster in the future scenarios within most of the bays. Maximum tidal velocities increased in all of the bays, especially in Grand Bay where velocities doubled under the highest SLR scenario. In addition, the ratio of the maximum flood to maximum ebb velocity decreased in the future scenarios (i.e., currents became more ebb dominant) by as much as 26% and 39% in Weeks Bay and Apalachicola, respectively. In Grand Bay, the flood-ebb ratio increased (i.e., currents became more flood dominant) by 25% under the lower SLR scenarios, but decreased by 16% under the higher SLR as a result of the offshore barrier islands being overtopped, which altered the tidal prism. Results from this study can inform future storm surge and ecological assessments of SLR, and improve monitoring and management decisions within the NGOM.

Abstract. Several aspects of feedback mechanisms associated with surf zone sandbar response have been characterized using bathymetric surveys, sampled approximately monthly over a 16-year period at the Army Corps of Engineers' Field Research Facility (North Carolina). The measured bathymetry was alongshore averaged and modeled by the superposition of two Gaussian-shaped sandbars on an underlying planar slope. A third, half-Gaussian-shaped bar represented steepening at the shoreline. The rms error between the measured bathymetry and the profile model was 0.10 m (estimated over 322 different surveys). The model explained 99% of the profile variance that remained after first removing the linear, cross-shore trend from each observed profile. Bar response, which was extracted from the modeled profiles, was compared to a local hydrodynamic forcing variable F (F was defined as the ratio of the wave height to water depth, evaluated at bar crest locations). At low values of F (i.e., nonbreaking conditions), bars migrated onshore, and their amplitude tended to decay. At high values of F (i.e., breaking conditions), bars migrated offshore, with relatively little change in amplitude. The transition between onshore and offshore migration occurred at a value of F that was consistent with the onset of wave breaking. Bar migration was associated with a stabilizing feedback mechanism, which drove bar crests toward an equilibrium position at the wave breakpoint. However, we observed that the rate of bar response showed no reduction for any nonzero choice of F, indicating that bars never reached equilibrium. Systematic bar amplitude decay was observed under nonbreaking conditions. Bar amplitude decay could drive F farther away from breaking conditions, allowing further bar amplitude decay. This is a destabilizing feedback mechanism, potentially leading to bar destruction.