Abstract Because of constraints in space and budget, lock capacity expansion projects often adopt a layout in which multiple locks share a common approach channel. To prevent excessive water-level fluctuations and ensure navigation safety, a staggered operation mechanism must be implemented. Traditional lock capacity calculation methods rely on a fixed average lockage duration, disregarding the randomness of lock operations and the forced waiting delays caused by staggered operation. Consequently, such methods systematically overestimate the number of lockages under staggered operation. To address this gap, this paper presents a stochastic simulation method for calculating the number of lockages that incorporates the randomness of lockage durations. Based on the cumulative distribution function of lockage durations, the inverse transform sampling method is employed to generate a random sequence of lockage durations. During simulation, if the time difference between the current emptying moment and the previous one is less than the preset staggered time (denoted as TC), the operation is delayed accordingly, and the delay time is accumulated. The proposed method is demonstrated through a case study of the three locks sharing a common approach channel in the Gezhouba lock capacity expansion project. For this case, the reduction in lockages (NRL) and cumulative delay time (TCD) are quantified under varying TC and number of operational days ( N ). The key findings for this specific site are: For TC = 6 and 15 min, the average daily reduction in lockages is approximately 0.3 and 1.8, respectively, with corresponding average daily cumulative delays of approximately 25 and 145 min. TCD exhibits an almost linear relationship with NRL, with a slope of 79 min, very close to the theoretical lockage duration. While the quantitative results are derived from this single-site case study, the proposed stochastic simulation framework is transferable to other multilock systems with appropriate site-specific input data.
Abstract With rising global temperatures, the poleward migration of mangroves may enhance their contribution to coastal protection. Although the wave attenuation capacity of Rhizophora stilt roots is well established, their influence on wave spectral transformation remains less understood. This study investigates wave height attenuation and associated spectral dissipation using a near-prototype physical model of a Rhizophora mangrove forest. Wave dissipation was quantified using a bulk damping coefficient ( β ), which increased under shallower water depths, higher relative wave height ( H / h ), and greater wave steepness ( H / L ), with the latter exhibiting a clear linear relationship. To better represent the complex projected area of the root system, a depth-averaged equivalent diameter ( d eq ) was introduced. Accounting for d eq improved the collapse of β across different water depths for H / h and H / L . When d eq was adopted as the characteristic length scale, the bulk drag coefficient ( C D ) correlated more strongly with the Keulegan–Carpenter number (KC) than with the Reynolds number ( Re ). An existing KC– C D relationship was expanded to include additional studies, with root diameter as the characteristic length scale demonstrating potential for a broadly applicable formulation for Rhizophora . A spectral analysis revealed preferential dissipation of high-frequency wave energy, particularly under emergent conditions, whereas this pattern diminished when the roots were fully submerged. This preferential dissipation was examined in the context of a frequency-dependent damping factor ( β f ). The increase of β f with frequency led to steepening of the spectral tail, particularly for steeper waves, which likely contributed to their greater attenuation.
This study assesses the feasibility of a beach and dune system as flood defense against storm surge along the coastlines of the Houston-Galveston area, proposed as a part of the Coastal Texas Project. We apply a semiempirical analytical model to predict dune erosion in a dual-dune system under changing climate conditions. Synthetic storms were simulated using validated hydrodynamic, wave, and hurricane models to produce input data (storm surge, wave height, and period) for the dune erosion model, reflecting both present day and future climate scenarios that incorporate projected sea level rise (SLR). Bias-correction techniques were applied to climate model output using historical observations of storm surge and wave data. An alternative sampling approach was also developed to stochastically predict dune erosion by integrating synthetic data into a copula-based framework. Results indicate that the annual-average dune erosion is approximately 8%-10% of system volume in the present scenario and increases to 33%-40% in future scenarios with higher SLR, leading to estimated dune rehabilitation cycles of 8-10 and 2-2.5 years, respectively. These findings suggest that, although the proposed beach and dune system is likely to be effective for storm surge protection under the present climate condition, significant adjustments will be desirable to maintain its resilience in the face of evolving climate and sea level rise. Importantly, bias correction of input data yielded substantial reductions in predicted storm surge and significant wave height, resulting in more accurate dune erosion predictions. This demonstrates the necessity of bias correction of hydrodynamic and wave parameters derived from global climate simulations for reliable coastal risk assessment and future planning. The copula sampling approach produced results comparable to the original results, which considered storms with extremely low or high occurrence probabilities, while providing lower sensitivity to bias-correction methods and copula generator types.
We present the methods and results for automatic identification system (AIS) vessel tracking data analysis to support port operations and simulation efforts in the Houston region. While AIS data have been widely used to measure port performance, we specifically study the validity of assuming a Poisson arrival process for the Houston anchorage and quantify observed anchorage waiting behavior for container, noncontainer cargo, and tanker vessels in a longitudinal analysis from 2019 to 2024. Statistical testing and graphical analysis are used to examine the interarrival times. We contend that the Poisson assumption is likely valid for container and noncontainer cargo vessel types, but less clear for tanker vessels. The queue analysis shows that the Houston anchorage is dominated by tanker vessels, of which a majority experience waiting, and that deviations in container vessel queue size and duration were observed in late 2021 and 2022, corresponding to the global demand surge for container cargo. These findings directly support simulation studies for the Port of Houston and provide empirical evidence of long-term vessel arrival and waiting behaviors in the Houston anchorage.
Celeris-WebGPU (version 1) is a browser-based interactive simulator for nearshore waves, designed to support coastal engineering design and natural hazard education. The system implements two depth-integrated, phase-resolving wave models: a standard mode solving the enhanced Boussinesq equations for weakly nonlinear weakly dispersive waves and a high-order mode solving the fully nonlinear extended Boussinesq equations for improved accuracy. The targeted use for the standard mode is rapid, iterative, and interactive simulations, while the high-order mode is best for design-level simulations in which accuracy is paramount. Both modes use a hybrid finite-volume-finite-difference approach; the standard mode's accuracy is of second-order in space, while the high-order mode's accuracy is of fourth-order. The simulation and visualization pipeline runs on the GPU via WebGPU, enabling faster-than-real-time performance on typical desktop hardware within a web browser. We show the accuracy and capabilities of Celeris-WebGPU with benchmark tests, including regular wave breaking on a beach, solitary wave run-up on a conical island, and nearshore wave transformation at Duck, North Carolina. An example design scenario adding a breakwater offshore of Oceanside, California, is presented, demonstrating the rapid prototyping capabilities of the platform. Results show good agreement with experimental and field data and illustrate how the WebGPU deployment allows interactive modeling with advanced physics directly in a web browser. The accessible framework of Celeris-WebGPU can broaden the use of physics-based wave simulation in both engineering practice and education.
The work presented here is part of a wider project funded by the Texas General Land Office to inform the development of sediment budgets for each of their four coastal regions. A numerical model is developed to simulate sediment transport along the southern coast of Texas. The model computes the waves currents and sediment transport in the area using the fully coupled openTELEMAC modeling system (version 8p1r1). This model is calibrated against measured data on waves, currents, and water levels. In these comparisons with measurements, the model skills range from sufficient in very calm conditions to very good in more energetic conditions. The sediment transport model predicts sedimentation rates in navigation channels that match both observed channel infill and long-term dredging records well. Conventional wisdom is that residual sediment transport along the south Texas shores is directed northward and simple calculations of potential wave-driven transport (littoral drift) in the nearshore have confirmed that to be the case. However, when the combined influence of waves and currents is taken into account in the regional modeling, the sediment transport modes and the respective pathways in the area have been found to be more complex than reported by previous authors. In summer, residual sediment transport is still to the north, but in nonsummer conditions, strong winds cause significant southward directed currents. These wind-driven currents in the Gulf of Mexico change the residual transport direction from northward to southward. Averaged over the year, this leads to sediment transport pathways with a divergence point about 50 km north of Mansfield Pass, with southward alongshore sediment transport south of this point and northward transport north of it. Although the literature assumes a drift convergence near Kenedy County, the modeling presented here identifies this area as a drift divide.
The Port of Houston, Texas, is regularly disrupted by recurring fog events that reduce visibility and result in navigation channel closures for safety reasons. These fog closures disrupt regional industry as no vessels may enter the port, a critical issue given the prominence of Houston as a freight gateway. Despite these significant impacts, fog events are relatively understudied in the port management literature. As arriving vessels wait in the anchorage area before visiting the port, anchorage queue behavior provides insights into the performance and resilience of the port system. We introduce conceptual definitions to capture the anchorage queue dynamics of a fog disruption and recovery cycle. Specifically, we define the queue growth rate, maximum recovery rate, and total recovery rate of the anchorage queue. We provide empirical evidence and derive typical values for these rates in the Houston anchorage using archival automatic identification system vessel tracking data and historic channel status records. Different fog events are categorized as single or compound events, and we compare the queue dynamics observed during fog events to those that occur during hurricanes, a more widely studied disruptive event. We identify 79 total fog events that resulted in measurable impacts on the Houston anchorage queue between 2016 and 2024. Anchorage queues typically grow at a rate of 0.82 vessels per hour (v/h) and recover at a total rate of 0.84 v/h, with a temporary ability to process vessels at a higher rate of 2.0 v/h after reopening postfog. Recovery rates show higher variance than growth rates, indicating that the system recovery is less predictable than its disruption. Our findings provide insights into maritime system performance and may help system stakeholders better understand and predict the impacts of fog events on vessel congestion and maritime freight operations.
An increase in sea levels and wave conditions due to climate change may affect the functionality of existing vertical seawalls. Submerged reef breakwaters are being used as retrofits to the existing vertical seawalls to control or prevent damage to coastal infrastructure from coastal flooding. The present study is based on laboratory experiments with a physical model to investigate the effectiveness of impermeable submerged breakwaters as a retrofit to vertical seawalls. This work considers a vertical seawall made of acrylic with a 1:15 smooth, impermeable foreshore slope. A total of 168 tests are considered, including regular and random waves. Random waves are generated using the Joint North Sea Wave Project (JONSWAP) spectrum. Each test consists of approximately 1,000 pseudorandom waves with a peak enhancement factor of 3.3. Key parameters, including wave run-ups, reflection characteristics, wave-induced pressures, wave forces, and wave overtopping, are measured for various wave conditions. The reductions in wave run-ups, forces, and mean wave overtopping are examined for different relative water depths and wave heights. The analysis of experimental measurements reveals a significant reduction in both horizontal forces and mean wave overtopping discharges for the given wave conditions. The new predictive relationships are proposed to evaluate the forces and the mean overtopping discharge of retrofitted vertical seawalls with submerged reef breakwaters.
Coastal regions are increasingly vulnerable to erosion, flooding, and habitat loss due to climate change-driven sea-level rise, intensified storm surges, and amplified wave energy, necessitating the development of innovative, sustainable coastal protection strategies. While traditional methods remain effective, they are often saddled with environmental drawbacks and high maintenance costs. The floating breakwater concept is a well-established coastal protection measure, offering adaptability to varying water depths and minimal ecological impact; however, optimizing its efficiency-particularly through advanced porous designs-remains an active research challenge. This study explores integrating triply periodic minimal surface (TPMS) geometries into breakwaters design to enhance and optimize their hydrodynamic performance and promote innovative coastal protection measures. Twelve three-dimensional-printed models with varying TPMS architectures, relative densities, and cell sizes (uniform and graded) were experimentally tested in a small-scale flume under 54 wave-current scenarios, analyzing wave reflection, transmission, and dissipation coefficients for their performance assessment. Unlike many previous studies that focused solely on waves, this research incorporates wave-current interactions to better simulate coastal environments. The results showed that diamond TPMS outperformed other geometries due to higher tortuosity and surface complexity, showing lower energy reflection and transmission and greater dissipation. Models with lower relative density and larger cell sizes (required up to 46% less material for printing) exhibited improved energy reflection performance. Additionally, cell grading proved effective in enhancing dissipation and minimizing wave transmission. The findings demonstrate the potential of TPMS-based floating porous breakwaters as a cost-effective, adaptable, and high-performance solution for future coastal protection systems.
The objectives of this study are to analyze accident patterns across Indonesian inland waterways from 2007 to 2025 and to build a fuzzy Bayesian Network (BN) for risk prediction and causal mapping. The Sustainable Development Goals (SDGs), established by the United Nations as a global framework for social, economic, and environmental development, provide the policy context for this work. Our study draws on system safety and probabilistic reasoning, positioning a BN as both an empirical tool and a theoretical lens. This framework allows us to capture uncertainty, nonlinear interactions, and hidden dependencies that are often overlooked by conventional regression. We analyze 1,257 recorded river accidents using SAS Studio (version 3.8), combining descriptive statistics, inferential tests, and a Na & iuml;ve Bayes classifier. We split the data set into training (70%) and testing (30%) partitions. Notably, we apply Laplace smoothing to stabilize sparse categories and evaluate accuracy via confusion matrices and area under the curve (AUC). Our analysis indicates that fatal incidents constitute 28% of total cases, with a marked increase after 2015. The BN model achieves an accuracy of 81% and an AUC of 0.84, outperforming logistic regression benchmarks. A closer look reveals that incident type and province consistently drive fatality probabilities, while seasonal patterns are surprisingly weaker than expected. This finding may reflect policy inertia in addressing high-risk routes rather than climatic cycles. Practically, the BN framework contributes to early warning systems for transport regulators. Theoretically, it refines accident modeling by integrating uncertainty and context-specific causal pathways. We demonstrate, for the first time in Indonesia, that Bayesian Network analysis meaningfully predicts river accident risks. This novelty bridges theory and practice, offering both methodological innovation and actionable policy guidance.
The USACE traditionally uses regular exam, before dredge, and after dredge hydrographic surveys and/or survey volume reports to estimate needed dredge material volume(s) to return federal navigation channels to authorized or maintained dimensions required for safe navigation. However, estimating navigability based solely on channel conditions fails to consider the dimensions of vessels transiting a channel, and how much of the channel depth vessels are observed to require. This study proposes a new metric, called the volume to dredge (V2D), to estimate necessary dredging effort that improves upon the vessel encroachment volume methodology by capturing how much material must be removed from a channel such that all transiting vessels have a specified margin of vessel clearance. Dredging and vessel traffic data for every deep-draft port in USACE's South Atlantic Division were analyzed from 2019 to 2022, assessing the V2D and comparing it to dredging records. Results indicate that V2D provides a refined, demand-driven approach to channel maintenance, offering maritime authorities an enhanced tool for optimizing dredging efforts. The study further expands upon previous research by incorporating a detailed review of navigation channel management; proposing an equation to determine if harbors are being over-, under-, or right-sized dredged based on these metrics; and discussing the policy implications.
Data assimilation improves the accuracy of numerical models by integrating observational data into model predictions, with the ensemble Kalman filter (EnKF) being a widely used technique. Despite its effectiveness, EnKF is often computationally demanding, which limits its practical use in large-scale, high-resolution, and real-time operational models. To overcome this limitation, the present study applied an alternative approach: optimal interpolation (OI). In OI, the Kalman gain was parametrized and precomputed based on prior model statistics, eliminating the need for extensive ensemble simulations. As a result, the computational cost of a data assimilation run becomes comparable to that of a stand-alone hydrodynamic simulation, making it suitable for real-world applications. This method was implemented in TELEMAC-3D, a three-dimensional hydrodynamic modeling software based on the shallow-water equations, and tested in the Scheldt Estuary, where measured salinity data were assimilated into an operational forecasting system supporting a tunnel immersion project near Antwerp of Belgium. The results show that the model maintains stable salinity fields, progressively reduces relative errors over time, and captures the main hydrodynamic conditions in the estuary. These findings demonstrate the potential of OI as a computationally efficient and accurate data assimilation method, suitable for both real-time forecasting and engineering applications in which efficiency and precision are critical.
The present study examines the influence of the width of a structural element on local scour induced by tsunami-like inundation using dam-break bores through a program of laboratory experiments. The effect of bore characteristics on the local scour depth is also investigated. Based on the flow dynamics involved in the sediment transport processes, the authors initially hypothesized that the scour depth at the front face may primarily be driven by downward flow and horseshoe vortices, and that it would not vary significantly with structure width. Moreover, greater variation in scour depth was anticipated to occur at the upstream corners, where both horseshoe and lateral vortices contribute to the scour process. To test these expectations, time series of scour depth progression at the center of the front face and the upstream corners of the structural element were obtained using video cameras installed inside the structure. The results indicate that the scour depth at the upstream corner is less sensitive to the width of the structure, whereas the scour depth at the center of the front face is significantly influenced by the width. It is also demonstrated that the maximum scour depth recorded around the structure occurs at the upstream corners of the structure. An important finding of this study is that the final (residual) scour depth observed around the structure is shallower than the maximum scour depth observed during the inundation phase, highlighting the need to conduct further laboratory experiments to better understand the mechanisms of local scour development, rather than relying solely on post-tsunami field surveys.
This paper presents a case study for the implementation of hazard-consistent probabilistic scenario optimization (HPSO) to guide the storm surge risk assessment for a St. Bernard Parish neighborhood in the Greater New Orleans area. The objective of HPSO is to identify a reduced-size ensemble of synthetic storm scenarios (i.e., storm events) that best represent the regional storm surge hazard, the latter quantified through the storm surge levels for specific annual exceedance probabilities across different locations of interest. This identification is achieved within the HPSO by leveraging some response proxy to guide the ensemble selection while simultaneously adjusting the weights for the chosen storms to attain a consistent hazard description to some desired target. Recently, a computationally efficient HPSO formulation was developed for applications over extended coastal regions, with thousands of locations of interest. This efficient HPSO implementation is discussed herein when updating the storm surge hazard assessment for the New Orleans area to offer a comprehensive validation in a practical setting for future flood studies, specifically examining the influence of using a response proxy within the HPSO, a topic unexplored in the past. The response proxy for the case study corresponds to ADCIRC surge predictions for 645 synthetic storms from a previous flood study for the region, developed by the US Army Corps of Engineers. The selection of a reduced storm ensemble with up to 40 storms is then considered for an updated flood hazard study, corresponding to a new ADCIRC grid, with improved mesh resolution and flood protection measure description. The performance of the HPSO is assessed by evaluating the hazard consistency achieved using the optimal storm subset identified from the response proxy and comparing it with the corresponding consistency that could have been achieved using the optimal storm subset of the same size obtained if information for the actual response (and not the response proxy) were utilized. The influence of key parameters for the HPSO implementation is also examined within this setting to support a comprehensive validation.
