Coastal floods are composed of multiple drivers that interact with coastline geometries and are difficult to differentiate. To investigate the effects of basin shapes on storm surge and tides, we study the landfall of ex-Typhoon Merbok in the east Bering Sea, Alaska. Water levels at 34 stations are detided with a new tide wavelet tool, CWT_Multi (Lobo et al., 2024), and the spatial and temporal shifts in tides and surge are interpreted via long-wave theory. Results show nonstationary semiannual trends in tidal constituents due to ice effects and large interannual variability in mean-sea level (20-40 cm). After removing non-stationary tidal forcing from observed water levels, estimates of the spatial evolution of storm surge are less influenced by residual tide-surge interaction than using standard harmonic analysis methods for detiding. Results reveal that surge magnitudes increased into Norton Sound (diurnal resonance) and decreased into Bristol Bay (semidiurnal resonance). Landward funneling into the mixed tidal basin of the Kuskokwim River estuary amplified surge while attenuating tides at rates that reveal a frequency dependent response. The frequency response is explained using a non-dimensional friction-convergence parameter space. Storm surge amplification is strongly affected by convergence magnitude and depends on surge duration. Thus, common ∼1–2-day storm surges increase flood exposure more for communities with diurnal tides than communities with semidiurnal tides. Moreover, the duration of flooding and peak water levels are strongly influenced by basin geometry, in addition to variations in storm properties and continental shelf geometry, underscoring their importance in storm surge prediction.

Abstract Swell propagation and transformation across variable bathymetry and coastal obstacles fundamentally control nearshore wave energy redistribution, sediment transport, and shoreline evolution. Conventional satellite altimetry cannot adequately resolve these dynamics, particularly refraction and diffraction as swell transition into shallow waters. Leveraging high‐rate (HR) observations from the Surface Water and Ocean Topography (SWOT) mission, we present the first spaceborne measurements of nearshore‐coastal wave transformation at sub‐kilometric scales. SWOT HR observations over the English Channel during Storm Mathis reveal wavelength shortening, directional shift and distinctive fan‐shaped diffraction patterns, including a 27.5° island‐induced diffraction signal undetected by operational models. Comparisons with in situ measurements and the Atlantic‐European Northwest Shelf (NWShelf) Reanalysis, a regional configuration of the WAVEWATCH III spectral wave model, validate the robustness of the SWOT‐derived wave parameters. This finding establishes SWOT HR data as a new observational capability for quantifying wave‐bathymetry interactions and the resultant redistribution of wave energy that conventional altimetry fails to resolve.

Extreme water levels in estuaries exhibit considerable variability on multiple spatial and temporal scales, driven by complex responses to coastal, hydrological, meteorological, geological, and anthropogenic factors. These factors complicate the prediction of high-water level events and their characteristics. Although extensive literature exists on hydroclimatic and coastal extremes, few studies conduct comprehensive statistical analyses that jointly consider multiple drivers and the spatiotemporal variability of high-water-level events in estuaries. This study addresses these gaps by developing a robust statistical framework to identify, characterize and attribute local and regional high-water-level events in the St. Lawrence River Estuary (Canada) over the period 1980–2023. Building on existing approaches, a Generalized Additive Model (GAM) formulation of non-stationary harmonic regression is used to decompose the water level variability at 11 gauging stations, with automated selection of covariates and tidal constituents. This decomposition quantifies the relative contributions of the underlying processes, enabling event classification according to their dominant coastal, tidal, or fluvial drivers. Distinct spatial patterns of tidal and non-tidal processes are observed along the fluvial estuary, and a specific transition zone is identified between dominant tidal and fluvial influences. The results indicate that non-stationary dynamics are distinct in different tidal and non-tidal frequency bands. The resulting event classification also demonstrated a different landward shift from coastal to hydrological drivers for tidal and non-tidal non-stationarity. These findings guide the statistical analysis and configuration of the hydrodynamic models used in meteorological forecasting, climate projections, and other specific applications.

Abstract. Tidal analysis and prediction are traditionally based on the harmonic decomposition of continuous water-level records. This limits the applicability to sparse, historical observations of high and low waters. Here, we adopt a high–low tidal analysis (HLTA) framework that directly models tidal extrema and their temporal modulation using lunar transit timing and astronomical forcing. Two formulations are explored: a long-period harmonic (LPH) approach and an empirical–astronomical (EA) representation. Application to tide-gauge data from the Western Scheldt demonstrates that HLTA predicts tidal extrema with accuracy comparable to harmonic analysis of 10-minute observations for water levels. Performance is also largely improved for the prediction of extrema timing, and bias is reduced. In contrast, harmonic analysis applied directly to high–low data performs poorly, not only due to aliasing, but also because of broad-scale dependencies between constituents introduced by sparse sampling. The HLTA framework is robust to observational errors and can be extended naturally to non-stationary conditions by incorporating, for example, river discharge. Coupled with simple interpolation, HLTA enables accurate reconstruction of the continuous tidal signal, matching or exceeding harmonic analysis on high-resolution data in shallow systems where the tidal wave is strongly distorted. These results demonstrate that accurate tidal reconstruction from high–low observations is feasible even in strongly distorted, shallow systems, with performance comparable to modern high-resolution analyses. This enables improved use of historical datasets for applications such as storm surge analysis, sea-level rise, and the analysis of changing tides, while also suggesting potential for improved modern tidal prediction in shallow and non-linear environments.

Estuarine and tidal river systems suffer from persistent observational gaps, as tide-gauge records are often short, discontinuous, or unevenly distributed. This study introduces a novel time-domain lag-regression (LR) framework to reconstruct continuous water-level records directly from neighboring observations, without relying on harmonic constants or external forcing variables. The method is evaluated in the St. Lawrence fluvial estuary (Canada) across multiple spatial configurations, calibration windows, and extreme events. Results show that LR accurately reproduces both tidal and non-tidal variability, extending incomplete records while preserving physical consistency. Incorporating Variational Mode Decomposition (VMD) further improves reconstruction skill under strongly non-stationary conditions by explicitly resolving frequency-dependent signal propagation. This proves especially beneficial for representing shallow water tides and for improving peak estimation during storm surges. Beyond gap filling, the approach supports data quality control by revealing discontinuities and inhomogeneities in historical records. Owing to its simplicity and limited data requirements, the framework is applicable to a wide range of estuarine, coastal, and inland systems, supporting long-term water-level analysis, extreme-event characterization, and flood-risk assessment. More broadly, the method provides a flexible basis for integration with non-stationary tidal analyses to investigate local water-level responses to regional drivers.

Coastal hydrodynamic models play a vital role in understanding and predicting flooding, but practical computational constraints and uncertainties in boundary conditions and bathymetry lead to systematic errors in local sea level. We show that much of this error is not random but reflects a stable, site-specific response that can be learned from model output and observations. We develop a time-invariant response operator combining linear memory and bilinear interactions to approximate unresolved shallow-water dynamics. In idealized experiments, the operator recovers overtide generation, tide–surge interaction, and fluvial coupling. The approach yields substantial improvements across regional and operational models of water level and currents, and the correction substantially reduces errors in coarse-resolution simulations, yielding skill comparable to higher-resolution models. Applied globally to a GTSM reanalysis, it reduces a 0.14 m negative bias in 100-year return levels to 0.02 m across 199 GESLA4 gauges, with mean absolute error reductions of 48%, 26%, and 20% for 10-, 50-, and 100-year return periods. These results show that a significant component of coastal model error, including extremes, stems from tidal processes and is locally learnable, offering a practical way to improve skill without modifying underlying models or increasing computational cost.

The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

The governmental Flood Hazard Identification and Mapping Program (FHIMP) seeks to update standards for flood mapping and risk area definition in Canada. Within this initiative, Environment and Climate Change Canada (ECCC) has been mandated to provide 2D simulations of water levels in the St. Lawrence fluvial estuary to estimate return periods of extreme water levels under historical and future conditions. Long-term fine-scale hydrodynamic simulations are necessary to reproduce accurately the complex interplay of hydrological, meteorological and tidal processes responsible for extreme water levels in this system. However, the substantial computational resources and time needed to run the hydrodynamic numerical models constrain the feasibility of producing numerous long-term simulations with a wide range of potential flood-generating conditions. Consequently, this study considers a complementary statistical framework to assess the extreme characteristics and drivers from historical data to prepare input scenarios for climatic projections. Event-based analyses of water level records are conducted at 18 stations across the St. Lawrence system using univariate and multivariate techniques to characterize the observed extreme dynamics and flood events. Specifically, univariate frequency analysis is applied at each station to quantify local flood risk based on approximately 400 extreme events observed in the Estuary between 1972 and 2022. Multivariate investigations based on a non-stationary tidal harmonic regression tool (NS Tide) are then used to study the system dynamics involved in major observed events and reconstruct the extreme water level series using a set of hydrological, meteorological, and astronomical covariates. Finally, multivariate spatial analyses are performed on the identified extreme events and NS Tide continuous reconstructions. The goal is to assess the characteristics of high water-level events (e.g., duration, seasonality, and probability distribution) and extreme drivers at the local and regional scales.

This study investigates the interactions between tides, storm surge, river flow, and power peaking in the microtidal Neretva River estuary, Croatia. Based on the existing NS_Tide tool, the study proposes a new non-stationary harmonic model adapted for microtidal conditions, which incorporates linear storm surge, as well as linear and quadratic river discharge terms. This model enhances the NS_Tide's ability to accurately predict water levels from tide-dominated sections downstream to discharge-dominated areas upstream. River discharge was identified as the dominant factor for predicting stage levels at most stations, while the influence of storm surge, though consistent, decreased upstream. Strong tide-river interactions were observed throughout the study domain, with the stationary tidal component consistently contributing to water level fluctuations at all locations, and minimal influence from the tide-surge interaction component. Simulations using the STREAM numerical model were also used to isolate the variability in water levels caused by power peaking. These simulations demonstrated that high-frequency discharge fluctuations due to hydropower plant operations amplify the S_1 constituent in upstream river sections and modulate the amplitudes of other tidal constituents in the estuarine and tidal river sections. The proposed method proved highly effective in the microtidal context of the Neretva River and shows potential for adaptation to mesotidal and macrotidal systems.

Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) is an emerging sensor technique that has become well-established for water level monitoring. While GNSS-IR has previously been employed for monitoring properties of lake ice and sea ice, it has not been applied for monitoring river ice. This paper presents results from monitoring river ice breakup at three sites in Canada. GNSS-IR data was compared to co-located time-lapse camera imagery and it was found that GNSS-IR signal was sensitive to periods where there is rough or broken ice in view of the sensor. Using data from Sentinel-1 and the RADARSAT Constellation Mission (RCM), the first ever comparison of GNSS-IR with Synthetic Aperture Radar (SAR) imagery is presented and a negative correlation of -0.8 is found between the GNSS-IR spectral power and SAR backscatter. Three classification algorithms of varying complexity (K-means clustering, neural network and random forest) are explored for detecting river ice using GNSS-IR. Using a shallow neural network with two hidden layers, an optimal accuracy of up to 94% is achieved over all three sites, or 97% when mixed water-ice conditions are excluded from the analysis. In summary, GNSS-IR has strong potential for ice monitoring applications, including monitoring the formation of ice jams.

Abstract. Coastal services are fundamental for society, with approximately 60 % of the world’s population living within 60 km of the coast. Thus, predicting ocean variables with high accuracy is a challenge that requires numerical models able to simulate from mesoscale to submesoscale processes, to capture shallow water dynamics influenced by wetting-drying and resolve the ocean variables in very high-resolution spatial domains. This paper introduceskey aspects of coastal modelling, such as vertical structure of the mixed layer depth, parameterization of bottom roughness and the dissipation of kinetic energy in coastal areas. It stresses the need for models to account forthe nonlinear interactions between tidal currents, wind waves, and small-scale weather patterns, emphasizing their significance in refining coastal predictions. In addition, observational advancements, such as high-frequency (HF) radar and satellite missions like SWOT, provide unique opportunities to observe coastal dynamics. This integration enhances our ability to model physical and dynamical peculiarities in coastal waters, estuaries, and ports. Coastal models not only benefit from such high-resolution observations but also contribute to evolving observational systems, creating feedback loops that refine monitoring and prediction capabilities. Modeling strategies are also examined, including downscaling and upscaling approaches, and numerical challenges like implementing robust data assimilation schemes to refine estimations of coastal ocean states are addressed. Emerging techniques, such as advanced turbulence closure models and dynamic vegetation drag parameterization, are highlighted for their role in enhancing the realism of modeled coastal processes. Furthermore, the integration of atmospheric forcing, tidal asymmetries, and estuarine dynamics underlines the necessity for models that span the complexities of the coastal continuum. It also demonstrates the critical importance of accurately modeling coastal and estuarine systems to capture interactions between mesoscale and submesoscale processes, their connections to broader oceanic systems, and their implications for sustainable coastal management and climate resilience. This work underscores the potential of advancing coastal forecasting systems through interdisciplinary innovation, paving the way for enhanced scientific understanding and practical applications.

Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the "response coefficient method") to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk-Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference ("dynamic inference") based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data.

The SWOT satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Here, we present the first discharge estimates from SWOT during the initial orbital configuration of the mission. A preliminary accuracy assessment from April of 2023 shows results consistent with pre-launch expectations: SWOT discharge can track dynamics without any gauge information. It captures discharge magnitude correctly in some cases but has bias in others. Correlations in a set of representative cases of SWOT performance ranged from 0.42 to 0.89, while the normalized root mean squared error was between 4.6% and 67.5%. Although we were very limited in the selection of river reaches, the feasibility of estimating river discharge from SWOT and these promising initial results are encouraging. As SWOT data are improved by reprocessing, we argue that the outlook for SWOT discharge is bright.

Estuarine salt marshes globally face numerous threats, not least of which include changing hydrological conditions from human alteration and climate change impact to river flows, sea levels and coastal processes. While changing inundation is evident in many systems, often, the detail of which estuarine processes are changing and to what extent they contribute to flooding and habitat distribution remains unknown. Water levels in the microtidal Swan River Estuary (Derbarl Yerrigan), Western Australia, which has experienced significant climate drying since the 1970's, were disaggregated to assess contributions from tides, mean sea level, barometric effects, river flows and river-tide interactions. These contributions were mapped to the habitat of a salt marsh community. The effect of declining river flows on tides were further assessed by wavelet and harmonic analyses. We found that tides and barometric effects presently dominate flooding events of relevance to the salt marsh community. Declining winter runoff resulted in an increase in the tidal amplitude in the upper estuary. There was also a positive winter mean sea level pressure trend, associated with the winter rainfall decline. Altogether, there was zero net change to flooding of a salt marsh in the estuary from these processes. Therefore, steady sea level rise masked changes in the relative contribution of flooding mechanisms in the estuary which have implications for the stability of the marsh ecosystem. Disaggregating process contributions to salt marsh water levels offers a means to better assess the hydrodynamic processes presently sustaining salt marsh communities and to inform how they might change in the future. These results show that numerous hydrological processes can interact to mask non-stationary changes to estuarine hydrology supporting salt marsh habitat.

Ship-generated waves are often amplified onshore in confined seaways and are associated with several incidents worldwide. Few tools enable modeling the ship waves' evolution through complex bathymetry. Here, we assess the skill of XBeach's ship module for simulating the primary wave generated by a moving pressure head. The model was validated for five ships against field observations at three stations across Lake Saint Pierre, the widest section of the St. Lawrence seaway between Quebec City and Montreal. The study was motivated by reported damages caused by a container ship transiting at 17.6 knots, that is, 20% faster than other ships during extreme flooding. Our model predicted that the ship involved in the incident created drawdown (<20 cm) and runup (<15 cm) that was twice as high as slower ships. However, simulating a wide range of water levels and ship speeds shows that the waves would have been larger at lower water levels due to shoaling. Nonetheless, XBeach could model the evolution of the waves' drawdown as they propagated over several kilometers from the channel.

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage‐constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Database of model output and field measurements associated with Lac St-Pierre ship wave study. Please see the readme for details of the NC files contained in this repository.

Understanding the alterations in spatial–temporal water level dynamics caused by natural and anthropogenic changes is essential for water resources management in estuaries, as this can directly impact the estuarine morphology, sediment transport, salinity intrusion, navigation conditions, and other factors. Here, we propose a simple triple linear regression model linking the water level variation on a daily timescale to the hydrodynamics at both ends of an estuary. The model was applied to the upper Yangtze River estuary (YRE) to examine the influence of the world's largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the estuary. It is shown that the regression model can accurately reproduce the water level dynamics in the upper YRE, with a root mean squared error (RMSE) of 0.061–0.150 m seen at five gauging stations for both the pre- and post-TGD periods. This confirms the hypothesis that the response of water level dynamics to hydrodynamics at both ends is mostly linear in the upper YRE. The regression model calibrated during the pre-TGD period was used to reconstruct the water level dynamics that would have occurred in the absence of the TGD's freshwater regulation. Results show that the spatial–temporal alterations in water levels during the post-TGD period are mainly driven by the variation in freshwater discharge due to the regulation of the TGD, which results in increased discharge during the dry season (from December to March) and a dramatic reduction in discharge during the wet-to-dry transitional period. The presented method to quantify the separate contributions made by changes in boundary conditions and geometry to spatial–temporal water level dynamics is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled or climate-driven estuaries worldwide.