Abstract. Climate warming is leading to the erosion of Arctic permafrost coastlines at accelerating rates, with erosion up to 20 m yr−1 along the Alaska Beaufort Sea. Yet, accurate predictions of this erosion require coupling thermal permafrost dynamics with coastal hydro-morphodynamics, and current models are either computationally prohibitive for long-term simulations or omit important physical processes. To address these issues, a new open-source Python-based model called Arctic-XBeach was developed to couple the morphodynamic model XBeach to a one-dimensional enthalpy-based thermal module. A focus of this first version of Arctic-XBeach is on thermal denudation, the dominant erosion mechanism at sites where elevated beaches limit direct wave–bluff contact. Unlike prior implementations of thermal modules coupled with morphodynamic models, ArcticXBeach uses an event-driven decoupling strategy. Specifically, the thermal module continues to calculate the evolution of the thawed layer during the year, but the morphodynamic module (XBeach) is only activated when simulated storms produce conditions under which thawed sediment is available for removal. Using this decoupled strategy results in a reduction of >99 % in the number of calculations required for each time step in comparison to continuous coupling of the two modules. Validation of the model was performed at Barter Island, Alaska. Observed temperature trends were well-reproduced by the model (RMSE < 1.5 K), and the modeled trends in shoreline & bluff recession matched the observed trends (RMSE of 2.0–4.1 m over evaluation periods spanning 2–3 years each, compared to a total observed retreat of ∼112 m over 70 years with peaks of 6.6 m yr−1). Therefore, Arctic-XBeach has the potential to be used to run seasonal to decadal simulations of thermal- denudation dominated permafrost coastal changes, and also provides a flexible platform to integrate other Arctic-specific processes. Thermal abrasion and block-failure processes are planned as future extensions.

Coastal regions across the globe, including the Salish Sea, are becoming increasingly vulnerable to compound flooding due to the interaction between storm surge, tides, and river outflow. This hazard is anticipated to increase under sea level rise and climate change. This research offers a high-resolution flood hazard mapping approach for King and Pierce Counties of Washington State (United States of America) using the SFINCS (Super-Fast INundation of CoastS) model to facilitate a Continuous Flood Response Modeling (CFRM) framework wherein decades of dynamic coastal and fluvial processes are simulated. By applying a cell-by-cell extreme value analysis, we predict flood areas for return periods of 1 to 100 years and compute the Expected Annual Flooded Area (EAFA) as a probability-weighted indicator of flood exposure. Validation of the model against NOAA and USGS gauge data demonstrated good skill (RMSE: 14-17 cm for coastal water levels; unbiased RMSE: 49-116 cm for river water levels), while comparison with FEMA Special Flood Hazard Areas showed high spatial agreement of flooding (hit rates: 0.75-0.83). The statistical analysis of the historical flooding timing showed that the 28 December 2022, event was primarily responsible for the majority of historical flooding in the region. Climate simulations for today indicate an EAFA range of 56-200 ha in King County and 250-644 ha in Pierce County. Projections of future changes show that the primary driver of increasing flood extent is sea level rise (an increase of 80 %-360 % with 1m SLR), while climate change drivers, such as changes to storm patterns, reduce hazards minimally. A threshold was also identified where there is a substantial increase in the area of land that is flooded when sea levels rise above 100-150 cm. Finally, it was found that simple deterministic flood maps may underrepresent flood hazard by approximately 0.5 m if not all contributing factors are considered. Therefore, these findings provide evidence supporting the use of integrated measures of flood hazard, such as EAFA, to inform more rational and spatially responsive flood risk management.

Abstract. Using high-resolution, ensemble-based modeling of the CMIP6 High Resolution Model Intercomparison Project, this study evaluates potential changes to extreme coastal water levels and waves across the Salish Sea, the largest estuary on the North American west coast. Results of a well-validated (water level error ~10 cm) regional hydrodynamic and wave model indicate that extreme water levels (e.g., 30-year return period) are projected to increase across the region, while lower-magnitude events (e.g., annual) are projected to slightly decrease. Wave height projections are spatially heterogeneous but generally decrease in southern basins and increase in the far north basins. Seasonal decomposition reveals a modest decrease in winter water levels and an increase in summer water levels. Non-linear interactions between sea level rise (SLR) and hydrodynamic response were assessed across seven SLR scenarios (0–3 m), revealing localized amplification effects up to 10 cm, though these remain minor relative to the SLR signal. Overall, uncertainty across the CMIP6 ensemble is high in this region, rendering most modeled changes statistically non-significant and emphasizing the need for studies to consider an ensemble when looking at climate projections. The dominance of tidal forcing in extreme water levels, coupled with high inter-model variability, indicates that near-term climate-induced changes to oceanic drivers are unlikely to significantly alter coastal hazards in the Salish Sea by 2050. These findings support the use of present-day conditions as a proxy for near-future planning and emphasize SLR as the primary contributor to changing future coastal risk in the Salish Sea.

Tropical cyclones (TCs) pose significant risks due to their associated hazards, including powerful winds, inland and coastal flooding, and wind waves. However, more reliable TC records are required to ensure a robust statistical analysis for risk assessment. To overcome this limitation, researchers have developed methods to generate synthetic tropical cyclones (STCs) that provide a larger sample size of occurrences at specific locations. This study compares STC databases from different sources such as Massachusetts Institute of Technology (MIT), Columbia HAZard model (CHAZ), Synthetic Tropical cyclOne geneRation Model (STORM), and Deltares with historical TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) on a basin-wide scale in the North Atlantic Basin. The aim is to assess the effectiveness of STCs in replicating crucial historical tropical cyclones parameters for risk analysis and to identify potential biases in the STC generation models. The comparison uses a hexagonal mesh to evaluate characteristics such as maximum winds, translation speed, and residence time. The study acknowledges the validation paradox arising from the limited IBTrACS data at specific locations that make it difficult to rigorously validate the accuracy of STCs in those areas and from systematic differences across the STC datasets. Despite the historical TCs database limitation, comparing STC with IBTrACS characteristics remains the only viable method for assessing biases in STC generation models. The evaluated STCs reveal spatial bias patterns, which may indicate deficiencies in the underlying hazard models. Identifying and describing these biases aim to guide the use of these events and highlight key aspects for further development in STC generation methods.

Coastal zones are areas of high economic and ecological value which are under threat of natural hazards such as flooding and erosion. Already 10percent percent of the world’s population lives below 10 meters above mean sea level (McGranahan et al., 2007) and this number is likely to grow in the future. Moreover, coastal zones will experience accelerated sea level rise and will be subject to potentially increasing intensities of extreme water levels, due to extra-tropical and tropical storms, which will aggravate the impact of natural hazards. These increased impacts and costs necessitate an approach to assess flooding and damages a priori for planning and mitigation purposes. It also calls for flood forecasting systems to alert authorities to be able to prepare for an impact and evacuate the population (Roy and Kovordányi, 2012). While a number of forecasting systems are available, these usually focus on a relatively small area and a small number of hazards, mostly total water level, and rarely impacts. This presentation is about upscaling to larger regions, including more physics and more impacts.

Low-lying coastal areas are highly vulnerable to flooding hazards. High marine water levels may overflow seawalls, render the storm drain system inoperable, and promote pluvial and wave overtopping flooding. Complex interactions between various coastal flooding drivers (marine water levels, precipitation, waves) and urban infrastructure (i.e., the stormwater system, and seawalls) are characterized using a novel, tightly coupled hydrodynamic model. Flood extent field observations of tidal overflow, pluvial flooding, and overtopping flooding, along with storm drain system pressure sensor data are used to evaluate hydrodynamic model performance. High marine water levels, precipitation, and overtopping events are modeled and compared with validation data. Results suggest the hydrodynamic model explicitly resolving both 1D storm drain pipe flow and 2D overland flooding more accurately simulates compound flooding compared to typical 2D overland flow models. Nonlinear compound effects were resolved by comparing combined univariate flood impact to corresponding compound flood impact modeled within a tightly coupled 1D2D infrastructure-resolving model. Projected flood extents were similar to 50% greater when compound interactions were resolved. Compound effects vary with event type, event magnitude, and site characteristics. Critically, coastal adaptation strategies protecting against high embayment water levels such as elevating seawalls may exacerbate compound flooding effects in low-lying communities.

Accurate flood risk assessments and early warning systems are needed to protect and prepare people in coastal areas from storms. In order to provide this information efficiently and on time, computational costs in flood models need to be kept as low as possible. One way to achieve this goal is to apply subgrid corrections to relatively coarse computational grids. Previously, these have been used in full-physics circulation models. In this paper, for the first time, we developed subgrid corrections for the linear inertial equations (LIEs) that account for bed level and friction variations. They were implemented in the Super-Fast INundation of CoastS (SFINCS) model version 2.1.1 Dollerup release. Pre-processed lookup tables that correlate water levels with hydrodynamic quantities make more precise simulations with lower computational costs possible. These subgrid corrections have undergone validation through several conceptual and real-world application scenarios, including rainfall-induced flooding during a hurricane and tidal propagation in an estuary. We demonstrate that the subgrid corrections for linear inertial equations significantly improve model accuracy while utilizing the same resolution without subgrid corrections. In terms of computational efficiency, subgrid corrections increase computational costs by 38 %–128 %. However, this yields a 35–50-time speedup since coarser model resolutions with subgrid corrections can provide the same accuracy as finer resolutions without subgrid corrections. Limitations are also discussed; for example, when grids do not adequately resolve river meanders, fluxes can be overestimated. Our findings show that subgrid corrections are a useful asset for hydrodynamic modelers striving to achieve a balance between accuracy and efficiency.

Hydrodynamic models are valuable tools for understanding the primary factors influencing daily and peak water levels and for guiding discussions on potential adaptation strategies for managing flood risk in coastal areas. This analysis uses the Delft3D San Francisco Bay-Delta Community Model to simulate water levels and incorporates the effects of a number of adaptation measures in the urban San Francisco Bay estuary, California. In particular, we examine the influence of shoreline hardening, nature-based solutions, and subregional floodgates on regional water levels. The result shows that under present conditions, tidal amplification is responsible for generating a wide distribution of extreme water levels across San Francisco Bay. Tidal amplification is found to decrease under sea level rise, thereby producing a relative damping effect on extremes. A comparison of different shoreline scenarios demonstrates that hard frontal shorelines result in higher tidal amplification, whereas restored (soft) shorelines lower amplification. The current shoreline configuration has both hard and soft characteristics and results in an intermediate tidal response. In some areas, wetland restoration reduces extreme water levels by as much as 20 cm, whereas hard-shoreline addition elevates them by as much as 10 cm for 1.5 m of sea level rise. Furthermore, local floodgates can significantly reduce high water levels without major adverse effects elsewhere in San Francisco Bay. These findings point toward the justification for a range of adaptive measures across political boundaries, weighing hard and soft options in addressing the mounting danger of sea level rise.

Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic drivers poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple drivers with correlations between some of them, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges; however, they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical-dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and machine learning-based surrogate models. The framework was demonstrated in San Francisco Bay (SF) over the past 500 years with accuracy similar to the physics-driven approach but with much higher computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and model of the hydrological and reservoir system. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding (e.g. surge, waves, river discharge, etc) in SF Bay. A process-based hydrodynamic model is built to predict total water levels varying in time and space throughout SF Bay based on stochastically generated drivers. The machine learning-based surrogate models are then developed from a relatively small library (several hundred) of hydrodynamic model simulations to efficiently predict water levels for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical-dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal-fluvial flooding, along with the relative contributions of drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.

Climate change is expected to impact hydrological regimes worldwide, including the Pacific Northwest of the United States. This study investigates how climate change will affect river discharge in the Puget Sound region of the State of Washington, with a focus on King and Pierce Counties. We simulated river discharge under historical and future conditions using the physically based, spatially distributed wflow_sbm hydrological model, which was calibrated and validated against U.S. Geological Survey discharge records. Future forcing was based on an ensemble of six high-resolution CMIP6 climate models, which were bias corrected using empirical quantile mapping. The results indicate a decrease in summer discharges (5–10%) and an increase in winter discharges (5–10%) across the study region. The high discharges (90th percentile) are projected to increase in winter, and the low discharges are projected to decrease in summer, due to shifts in precipitation regimes, snowpack hydrology, and evapotranspiration. However, variability between individual CMIP6 models often exceeds the magnitude of ensemble mean changes, underscoring substantial uncertainty in climate projections and the importance of including multiple climate models in climate change analysis. Furthermore, model consensus increased with elevation, which could be the result of the higher elevation areas being driven by less diverse hydrological processes. These findings highlight potential challenges for regional water management, ecosystem health, and flood risk mitigation in the Puget Sound region under future climate conditions.

First posted February 1, 2024 For additional information, contact: Pacific Coastal and Marine Science CenterU.S. Geological Survey2885 Mission St.Santa Cruz, CA 95060 Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Hawaiian, Mariana, and American Samoan Islands as a result of climate change and sea-level rise. We followed a hybrid (dynamical and statistical) downscaling approach to map flooding due to waves and storm surge at 10-square meter resolution along all 1,870 kilometers of these islands’ coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.00 meter (m), +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios. We quantified the coastal flood depths and extents using the latest climate forcing from Intergovernmental Panel for Climate Change’s Sixth Assessment Report Coupled Model Intercomparison Project. The data generated using these methods provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when climate change and sea-level rise increase coastal storm-induced flooding to help identify areas where management and (or) restoration could potentially help reduce the risk to, and increase the resiliency of, the coastal communities in the populated Hawaiian, Mariana, and American Samoan Islands.

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones in coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are thirty times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more area will be flooded, regardless if that flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms.

Coastal flooding affects low-lying communities worldwide and is expected to increase with climate change, especially along reef-lined coasts, where wave-driven flooding is particularly prevalent. However, current regional modeling approaches are either insufficient or too computationally expensive to accurately assess risks in these complex environments. This study introduces and validates an improved computationally efficient and physics-based approach to compute dynamic wave-driven regional flooding on reef-lined coasts. We coupled a simplified-physics flood model (SFINCS) with a one-dimensional wave transformation model (XBeach-1D). To assess the performance of the proposed approach, we compared its results with results from a fully resolving two-dimensional wave transformation model (XBeach-2D). We applied this approach for a range of storms and sea-level rise scenarios for two contrasting reef-lined coastal geomorphologies: one low relief area and one high relief area. Our findings reveal that SFINCS coupled with XBeach-1D generates flood extents comparable to those produced by XBeach-2D, with a hit rate of 92%. However, this method tends to underpredict the flood extent of weaker, high-frequency storms and overpredict stronger, low-frequency storms. Across scenarios, our approach overpredicted the mean flood water depth, with a positive bias of 7 cm and root mean square difference of 15 cm. Offering approximately 100 times greater computational efficiency than its two-dimensional XBeach counterpart, this flood modeling technique is recommended for wave-driven flood modeling in scenarios with high computational demands, such as modeling numerous scenarios or undertaking detailed regional-scale modeling.

First posted March 13, 2024 For additional information, contact: Pacific Coastal and Marine Science CenterU.S. Geological Survey2885 Mission St.Santa Cruz, CA 95060 Alaska's Arctic coast has some of the highest coastal erosion rates in the world, primarily driven by permafrost thaw and increasing wave energy. In the Arctic, a warming climate is driving sea ice cover to decrease in space and time. A lack of long-term observational wave data along Alaska's coast challenges the ability of engineers, scientists, and planners to study and address threats and effects from wave-driven erosion and flooding. To overcome the lack of available observational wave data in the nearshore in this study by the U.S. Geological Survey, waves were downscaled with the Simulating WAves Nearshore numerical wave model (SWAN) for the hindcast period of 1979 to 2019 from the United States-Canada border to the Bering Sea utilizing nine model domains. For each domain, the model was forced at the open boundary with 2,500 representative "sea states," which are likely combinations of significant wave heights, mean wave periods, mean wave directions, and wind speeds and directions. The sea states were obtained from the European Centre for Medium-Range Weather Forecasts "ERA5" dataset for reanalysis of winds and waves using a multivariant maximum-dissimilarity algorithm. The SWAN runs created a downscaled wave database at each grid point, which was used to reconstruct the 40-year time series in the nearshore along the 5- and 10-meter isobaths at locations approximately 400 m apart and corresponding to transects spaced approximately 50 m alongshore, as developed for USGS shoreline-change assessments. Reconstructed time series were compared to observations to validate the numerical model and the downscaled wave database method and showed overall good agreements.

Here, we describe a dataset of two-dimensional (2D) XBeach model files that were developed for the Coastal Storm Modeling System (CoSMoS) in northern California as an update to an earlier CoSMoS implementation that relied on one-dimensional (1D) modeling methods. We provide details on the data and their application, such that they might be useful to end-users for other coastal studies. Modeling methods and outputs are presented for Humboldt Bay, California, in which we compare output from a nested 1D modeling approach to 2D model results, demonstrating that the 2D method, while more computationally expensive, results in a more cohesive and directly mappable flood hazard result.

Accelerating sea level rise (SLR) and changing storm patterns will increasingly expose barrier islands to coastal hazards, including flooding, erosion, and rising groundwater tables. We assess the exposure of Cape Lookout National Seashore, a barrier island system in North Carolina (USA), to projected SLR and storm hazards over the twenty-first century. We estimate that with 0.5 m of SLR, 47% of current subaerial barrier island area would be flooded daily, and the 1-year return period storm would flood 74%. For 20-year return period storms, over 85% is projected to be flooded for any SLR. The modelled groundwater table is already shallow (< 2 m deep), and while projected to shoal to the land surface with SLR, marine flooding is projected to overtake areas with emergent groundwater. Projected shoreline retreat reaches an average of 178 m with 1 m of SLR and no interventions, which is over 60% of the current island width at narrower locations. Compounding these hazards is subsidence, with one-third of the study area currently lowering at > 2 mm/yr. Our results demonstrate the difficulty of managing natural barrier systems such as those managed by federal park systems tasked with maintaining natural ecosystems and protecting cultural resources.

Abstract. Accurate flood risk assessments and early warning systems are needed to protect and prepare people in coastal areas from storms. In order to provide this information efficiently and on time, computational costs need to be kept as low as possible. Reduced-complexity models using linear inertial equations and subgrid approaches have been used previously to achieve this goal. In this paper, for the first time, we developed a subgrid approach for the Linear Inertial Equations (LIE) that account for bed level and friction variations. We implemented this method in the SFINCS model. Pre-processed lookup tables that correlate water levels with hydrodynamic quantities make more precise simulations with lower computational costs possible. These subgrid corrections have undergone validation through a variety of conceptual and real-world application scenarios, including analyses of hurricane hazards and tidal fluctuations. We demonstrate that the subgrid corrections for Linear Inertial Equations significantly improve model accuracy while utilizing the same resolution without subgrid corrections. Moreover, coarser model resolutions with subgrid corrections can provide the same accuracy as finer resolutions without subgrid corrections. Limitations are discussed, for example, when grids do not adequately resolve river meanders, fluxes can be overestimated. Our findings show that subgrid corrections are an invaluable asset for hydrodynamic modelers striving to achieve a balance between accuracy and efficiency.