Abdolmajid Mohammadian

Land subsidence can substantially amplify future typhoon-induced coastal flooding, yet the effects of cessation timing and the effectiveness of coastal adaptation measures remain insufficiently quantified. This study assesses coastal flooding from a Nesat-like event in Metro Manila by 2100, using climate warming and sea level rise (SLR) under SSP2-4.5 and SSP5-8.5 as baselines for evaluating land subsidence and adaptation. Spatially variable subsidence rates derived from Sentinel-1A observations using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique were incorporated into TELEMAC-2D to evaluate three pathways: persistent subsidence, delayed cessation, and early cessation. Persistent subsidence increases inundation extent by nearly half relative to warming-SLR cases without subsidence. Earlier cessation substantially limits this additional inundation, showing that cessation timing strongly influences future flood expansion. Adaptation experiments indicate diminishing additional reductions as seawall height and mangrove greenbelt width increase. A hybrid seawall-mangrove configuration provides the greatest reduction among the tested measures, decreasing inundation extent by 70.5% relative to the no-adaptation case. These findings demonstrate the importance of representing spatially variable land subsidence and cessation timing in future coastal flood projections and support consideration of timely subsidence management alongside coastal protection in long-term adaptation planning for subsiding coastal cities.

Laboratory experiments and numerical modeling are essential tools for understanding the performance of engineered tsunami mitigation measures, enabling controlled investigation of complex hydrodynamic processes that are difficult to capture in real events. This review critically evaluates current research on key structural countermeasures, seawalls, breakwaters, and water-filled canals, focusing on findings from physical modeling and computational simulations. Evidence from numerical and laboratory studies demonstrates that properly designed mitigation structures can reduce tsunami wave energy, delay inland inundation, and decrease forces on downstream infrastructure. The effectiveness of these measures is strongly influenced by structural geometry, placement, and maintenance, as well as by accurate representation of flow dynamics in experiments and simulations. Despite significant advances, important gaps remain, including the validation of numerical models against high-fidelity experiments, the assessment of extreme events, and the evaluation of hybrid or integrated strategies combining multiple mitigation measures. This review identifies these gaps and highlights research priorities aimed at improving predictive capabilities, optimizing structural designs, and supporting the development of reliable, scalable, and context-specific tsunami mitigation solutions.

Tourist evacuation in tsunami-prone coastal communities is strongly shaped by local road knowledge acquired before a warning is issued. This dependence becomes especially important where external guidance is limited, damaged, or unavailable during a near-field tsunami emergency. Existing evacuation models often represent tourists through static familiarity classes, complete ignorance, or simple heuristic rules. These assumptions limit their ability to capture how pre-disaster sightseeing, memory degradation, and route recall affect evacuation behavior. This study develops an agent-based evacuation framework that represents tourist road cognition as a dynamic route-memory state. Tourists first explore a road network, encode experienced road links, undergo empirically calibrated cue perturbation, and retrieve usable route knowledge through Hopfield-network associative recall. The framework is tested on an idealized (10×10) grid and then applied to Tofino, British Columbia, using GIS-derived road geometry, zoning-based population placement, tourist sightseeing areas, and actual evacuation destinations. The simulations show that incomplete route memory can substantially reduce tourist shelter arrival within a guidance-free near-field tsunami response window, while full local road knowledge improves but does not eliminate the tourist evacuation shortfall. The results link tourist evacuation outcomes to remembered route availability, warning-time location, milling delay, and congestion on limited corridors. The framework provides a way to examine where route memory constrains evacuation and where information-support strategies may improve planning in tourism-oriented coastal communities.

Tsunamis, though infrequent, pose catastrophic threats to coastal communities and infrastructure, underscoring the urgent need for effective mitigation strategies. This review provides a comprehensive evaluation of current engineered countermeasures, specifically seawalls, breakwaters, and water-filled canals, by synthesizing findings from post-tsunami field surveys. Evidence from major events, such as the 2011 Tohoku Tsunami, indicates that well-designed structural measures can substantially reduce tsunami wave energy and delay inland inundation, thereby improving the resilience of coastal infrastructure. However, their effectiveness depends heavily on proper geometric configuration, construction quality, regular maintenance, and integration with non-structural measures such as evacuation planning. Recent experimental and numerical investigations have also highlighted the potential of innovative countermeasures, in attenuating bore forces and protecting critical infrastructure. Despite advancements, significant knowledge gaps remain, particularly concerning the long-term reliability, cost-effectiveness, and adaptability of these systems under diverse tsunami conditions. This critical review aims to identify these gaps and outline key priorities for future research and development. The findings contribute to a deeper understanding of structural tsunami mitigation and support the advancement of integrated, resilient strategies for reducing disaster risk in tsunami-prone regions worldwide.

Flooding is a complex, unpredictable disaster that occurs frequently and can have devastating impacts. Over the past two decades, the advent of machine learning (ML) methods has led to a surge in studies focused on flood prediction, emphasizing high-performance algorithms and fast processing times. The present study aims to investigate the challenges of generalization in flood prediction models using machine learning techniques. A dataset of 18,810 samples was compiled from 40 river basins covering the period 1959–2020. Nine machine learning algorithms were applied to the analysis: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Extra Trees, and Gaussian Naive Bayes. Four distinct validation methods were employed to assess the performance of the models, and the results were thoroughly analyzed. The Gradient Boosting model demonstrated exceptional validation performance indicating its robustness across diverse datasets. High accuracy was also observed in the Decision Tree, Random Forest, Extra Trees, and AdaBoost models. However, for datasets with fewer than 200 samples, these four models experienced a decline in performance. Elevation was identified as the most important factor influencing flooding in 36 basins. NDVI was the dominant factor in 3 basins, while rainfall was the main driver in only 1 basin. The results highlight the contributions and shortcomings of machine learning methods in sustainable flood disaster management systems.

Riverine fish conservation in eco‐hydraulics is often based on restoring or regulating river habitat to a semi‐natural state. For example, ecohydraulic projects support fish populations by increasing river connectivity through fishways, and by maintaining or restoring natural flow variance to enhance habitat quality and availability. However, the application of these concepts is challenging due to climate change and human perturbations that can modify the riverine environment in unpredictable ways. To address these challenges, we propose broadening eco‐hydraulics to eco‐evo‐hydraulics by incorporating the evolutionary dynamics of fish populations into eco‐hydraulics. We argue that this may improve the resilience and resistance of fish populations to future climate and human perturbation. Moreover, the evolutionary response of fishes to perturbations also alters their capability to “zoogeomorphically engineer” their physical environment, with associated feedbacks such as changes in sediment mobilization. Thus, understanding such hydro‐evolutionary feedbacks can improve the long‐term prediction of river hydraulics. In this position paper, we explore the interactions between hydraulics, hydrology, and rapid evolution based on a literature review to identify gaps and opportunities that need joint efforts from water resources engineers and evolutionary biologists. We propose the concept of eco‐evo‐hydraulics with hydro‐evolutionary feedbacks to encapsulate the interactions between the three disciplines, and suggest research avenues for the future. Finally, we suggest potential managerial strategies based on the eco‐evo‐hydraulic concept for local and basin‐wide fish conservation. We aim to fill the gap between water resources management and evolutionary biology to provide a new theory for fish conservation.