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The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast. ENSO end members El Niño and La Niña are linked to elevated coastal hazards across the Pacific region. Here, the authors show that the wave conditions and coastal response for the 2015–16 El Niño indicate that it was one of the most significant events of the last 145 years.

Multivariate coastal flooding is characterized by multiple flooding pathways (i.e., high offshore water levels, streamflow, energetic waves, precipitation) acting concurrently. This study explores the joint risks caused by the co-occurrence of high marine water levels and precipitation in a highly urbanized semi-arid, tidally dominated region. A novel structural function developed from the multivariate analysis is proposed to consider the implications of flood control infrastructure in multivariate coastal flood risk assessments. Univariate statistics are analyzed for individual sites and events. Conditional and joint probabilities are developed using a range of copulas, sampling methods, and hazard scenarios. The Nelsen, BB1, BB5, and Roch–Alegre were selected based on a Cramér–von Mises test and generally produced robust results across a range of sampling methods. The impacts of sampling are considered using annual maximum, annual coinciding, wet-season monthly maximum, and wet-season monthly coinciding sampling. Although annual maximum sampling is commonly used for characterizing multivariate events, this work suggests annual maximum sampling may substantially underestimate marine water levels for extreme events. Water level and precipitation combinations from wet-season monthly coinciding sampling benefit from a dramatic increase in data pairs and provide a range of physically realistic pairs. Wet-season monthly coinciding sampling may provide a more accurate multivariate flooding risk characterization for long return periods in semi-arid regions. Univariate, conditional, and bivariate results emphasize the importance of proper event definition as this significantly influences the associated event risks.

Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection.

Coastal flooding is a significant and increasing hazard. There are multiple drivers including rising coastal water levels, more intense hydrologic inputs, shoaling groundwater and urbanization. Accurate coastal flood event prediction poses numerous challenges: representing boundary conditions, depicting terrain and hydraulic infrastructure, integrating spatially and temporally variable overtopping flows, routing overland flows and incorporating hydrologic signals. Tremendous advances in geospatial data quality, numerical modeling and overtopping estimation have significantly improved flood prediction; however, risk assessments do not typically consider the co-occurrence of multiple flooding pathways. Compound flooding refers to the combined effects of marine and hydrologic processes. Alternatively, multiple flooding source–receptor pathways (e.g., groundwater–surface water, overtopping–overflow, surface–sewer flow) may simultaneously amplify coastal hazard and vulnerability. Currently, there is no integrated framework considering compound and multi-pathway flooding processes in a unified approach. State-of-the-art urban coastal flood modeling methods and research directions critical to developing an integrated framework for explicitly resolving multiple flooding pathways are presented.

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

The 2015–2016 El Niño provided insight into how low-inflow estuaries might respond to future climate regimes, including high sea levels and more intense waves. High waves and water levels coupled with low rainfall along the Southern California coastline provided the opportunity to examine how extreme ocean forcing impacts estuaries independently from fluvial events. From November 2015 to April 2016, water levels were measured in 13 Southern California estuaries, including both intermittently closed and perennially open estuaries with varying watershed size, urban development, and management practices. Elevated ocean water levels caused raised water levels and prolonged inundation in all of the estuaries studied. Water levels inside perennially open estuaries mirrored ocean water levels, while those inside intermittently closed estuaries (ICEs) exhibited enhanced higher-high water levels during large waves, and tides were truncated at low tides due to a wave-built sand sill at the mouth, resulting in elevated detided water levels. ICEs closed when sufficient wave-driven sand accretion formed a barrier berm across the mouth separating the estuary from the ocean, the height of which can be estimated using estuarine lower-low water levels. During the 2015–2016 El Niño, a greater number of Southern California ICEs closed than during a typical year and ICEs that close annually experienced longer than normal closures. Overall, sill accretion and wave exposure were important contributing factors to individual estuarine response to ocean conditions. Understanding how estuaries respond to increased sea levels and waves and the factors that influence closures will help managers develop appropriate adaptation strategies.

Infragravity (IG) waves are low-frequency water waves, which can propagate into harbors and estuaries, affecting currents and sediment transport processes. Understanding and predicting IG oscillations inside harbors and estuaries is critical to coastal management and estimating future resilience to climate change impacts. High-resolution water level and flow velocity observations collected within Seal Beach Wildlife Refuge in Southern California are analyzed for IG energy related to atmospheric parameters, water levels, and offshore wave conditions. A proof of concept approach for predicting infragravity oscillations within an estuary using machine learning (ML) is presented.

SedWaveFoam, an OpenFOAM-based two-phase model that concurrently resolves the free surface wave field, and the bottom boundary layer is used to investigate sediment transport throughout the entire water column. The numerical model was validated with large-scale wave flume data for sheet flow driven by shoaling skewed-asymmetric waves with two different grain sizes. Newly obtained model results were combined with previous nonbreaking and near-breaking wave cases to develop parameterization methods for time-dependent bed shear stress and sediment transport rate under various sediment sizes and wave conditions. Gonzalez-Rodriguez and Madsen (GRM07) and quasi-steady approaches were compared for intra-wave bed shear stress. The results show that in strongly asymmetric flows, considering the separated boundary layer development processes at each half wave-cycle (i.e., GRM07) is essential to accurately estimating bed shear stress and highlights the impact of phase-lag effects on sediment transport rates. The quasi-steady approach underpredicts (∼60%) sediment transport rates, especially for fine grains under large velocity asymmetry. A modified phase-lag parameter, incorporating velocity asymmetry, sediment stirring, and settling processes is proposed to extend the Meyer-Peter and Mueller type power law formula. The extended formula accurately estimated the enhanced net onshore sediment transport rate observed under skewed-asymmetric wave conditions.

Schubert, J.E.; Gallien, T.W.; Majd, M.S., and Sanders, B.F., 2015. Terrestrial laser scanning of anthropogenic beach berm erosion and overtopping. Anthropogenic berms are widely deployed to manage coastal flooding. The dynamic erosion of scraped berms exposed to waves and a rising tide in southern California was monitored with a terrestrial laser scanner (TLS) on three occasions in February and March of 2012. An improved characterization of initial berm geometry and the dynamics of berm erosion was pursued to accurately predict the onset and impact of coastal flooding associated with berm erosion and overtopping. TLS is shown to yield a digital terrain model (DTM) with a vertical accuracy of ca. 3 cm, indicating it is an excellent source of data for initializing mechanistic and/or empirical models that could be used to predict the onset and rate of wave overtopping. Minimum scan point spacings required to achieve this level of accuracy are investigated and reported. Additionally, a dimensionless water level representing the fractional submergence of the berm is identified as a good predictor of cumulative berm erosion under the test conditions.

Stanley, J.-D. and Corwin, K.A., 2013. Measuring strata thicknesses in cores to assess recent sediment compaction and subsidence of Egypt's Nile Delta coastal margin. Coastal flood risk in California is concentrated around urbanized embayments that are protected by infrastructure, such as levees, pumps, and flood walls, which pose a challenge to accurate flood prediction. A capability to predict coastal urban flooding at the parcel-scale (individual home or street) from high ocean levels (extreme high tides) is shown here by coupling a regional ocean forecasting system to an embayment-scale hydrodynamic model that incorporates detailed information about flood defenses. A unique flooding data set affords the rare opportunity to validate model predictions and allows us to identify model data that are essential for accurate forecasting. In particular, results show that flood defense height data are critical, and here, that information is supplied by a Real Time Kinematic Global Positioning System (RTK-GPS) survey, which yields ca. 1-cm, vertical root mean-squared error accuracy. Bathymetry surveys and aerial Light Detection and Ranging (LIDAR) data characterizing the embayment also prove essential. Moreover, hydrodynamic modeling of flood inundation is shown to significantly improve on planar surface models, which overestimate inundation, particularly when manipulated to account for run-up in a simplistic way. This is attributed to the transient nature of overtopping flows and motivates the need for dynamic, spatially-distributed overtopping models that are tailored to the urban environment.