Search Results (1,983)

This article presents an integrated design studio pedagogy for flood-resilient urban waterfronts that employs groundscape strategies, treating the ground as an active design medium to generate hybrid structures integrating landscape, architecture, and infrastructure. Implemented at the Fay Jones School of Architecture and Design (Fall 2024), the studio challenged students to transform North Little Rock’s flood-vulnerable riverfront by replacing conventional levee infrastructure with ground-based public architectural interventions. The study adopts a pedagogical case-study approach, examining a studio cohort in which all projects were developed under shared site conditions, design constraints, and instructional frameworks. Five assignments progressed from collaborative precedent analysis to individual technical development, integrating computational modeling, performance simulations, and expert consultations across structural, envelope, MEP, and site engineering. Student work is analyzed through comparative sectional diagrams and selected in-depth project studies to evaluate how groundscape functioned as a shared solution type for multiscalar integration. The results show that groundscape operates productively when tested against specific site constraints rather than deployed as a generalized esthetic. In response to flood elevations, degraded ecology, and limited public access, students developed distinct ground-based operations—such as embedding, lifting, and integrating flood walls as spatial thresholds—demonstrating architecture’s capacity to mediate between civic space, environmental performance, and flood protection. By situating groundscape within a problem-oriented pedagogy, the study consolidates modernist, postmodern, and contemporary groundscape discourse and demonstrates how architectural education can engage productively with climate-adaptation challenges. Full article

Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74−0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

Oceanfront relief varies along coastlines and serves as the first barrier to wave and surge damage. However, forecasted increases in storm frequency and sea levels are anticipated to enhance coastal erosion, potentially weakening this protection. The land–sea transition is variable along the New England coast, USA, and this variability has produced a range of coastal morphologies that can vary over short distances. It is important to track the beach transition zone to better understand transformations of the system and related hazard risks. A combination of field and computer-based methods was used to evaluate the beach transition zone of southwestern Rhode Island to determine alongshore variability and dynamics. More specifically, a decadal-scale study was conducted to examine changes in morphology from 2011 to 2022, and a short-term study at South Kingstown Town Beach examined changes from November 2023 to January 2024 using time-series drone-derived elevations. Classification of over 500 cross-shore transects illustrated the dominance of sedimentary shorelines, with smaller areas of rocky outcrops and hardening. Analysis of four different years (2011, 2014, 2018, and 2022) determined that beaches with dune morphology were the most common type of transition zone (41–47% of the transects) and transects with a high bank upland were the next most frequent class (34–41%). Following Hurricane Sandy in 2012, a 6% decrease in the number of dune-classified transects was measured; however, one-third of those recovered dune morphology by 2022. The greatest beach transformations over the short-term study occurred in response to strong storms in the 2023–2024 winter season, during which lateral beach movement (erosion) exceeded 15 m in portions of South Kingstown Town Beach. Dune erosion was accompanied by overwash flooding and deposition, and the area remained low-lying and thus vulnerable to future impacts. The beach transition zone classification and insights from this research will be informative for future planning by coastal communities by determining at-risk shorelines based on underlying geology and the stability of morphological features. Full article

This systematic review examines the effect of pre-incident information on public preparedness prior to an emergency or disaster. Preparing members of the public for adverse events can improve self-sufficiency and improve health outcomes, particularly during periods when emergency responders are not immediately available. Twenty-three studies were identified, addressing both natural and human-influenced events. All the studies investigated pre-incident training targeting members of the public rather than specialist responders. The synthesis considered training content, delivery approaches and evaluation methods. The studies included preparation, personal safety, triage, first aid and evacuation in scenarios involving terrorism, fire, earthquake, flood and CBRN events. Pre-incident education generally improves knowledge and intention to act, with higher-intensity and interactive training yielding greater engagement and response. Due to the difficulty of simulating emergencies and disasters, several studies used self-reporting and hypothetical testing, while others attempted to create real-life scenarios. The immediate effects of pre-incident education were generally positive, although many studies tested outcomes theoretically or within a classroom environment. It was also noted that few studies considered retention over the medium to long term; this is a concern as temporal decay may reduce preparedness. This review provides a basis for continued development of public-facing pre-incident education to increase resilience to both terrorist attacks and natural disasters. Full article

Floodplain forests in central Amazonia are structured along a marked flooding gradient that influences species distribution, performance, and survival. This study evaluated the demographic structure, survival, and growth responses of two co-occurring tree species across contrasting várzea environments differing in inundation regimes. Field surveys quantified seedlings, juveniles, and adults in low- and high-floodplain forests, while a field experiment assessed survival and growth under conditions with and without interspecific interaction. Repeated-measures ANOVA revealed that temporal variation and forest type significantly affected growth parameters, with species-specific responses to flooding intensity. In the field experiment, mortality of Crateva tapia L. differed significantly among treatments (χ² = 24.96, p < 0.001), with the highest mortality observed in high-várzea (up to 75% under interspecific interaction), while Hura crepitans L. showed 100% survival across all treatments. Non-parametric analyses detected no significant treatment effects on selected morphological traits. The results support the stress-gradient hypothesis, suggesting that plant–plant interactions may shift along the flooding gradient, with facilitative processes becoming more relevant under higher stress conditions. Overall, differential flood tolerance appears to be a key driver of habitat preference and population structure in these Amazonian wetlands. Full article

Based on Weibo big data, BERTopic, and dual-channel sentiment analysis model, a dynamic analysis framework of public perception and emotion evolution is constructed from the perspective of disaster chain and public response. The results show that (1) Due to the trust of information sources by the public, the efficiency of early warning information reaching the public and attracting attention is relatively high. Social media activity on related topics peaked several times in response to reports of major hazards, such as railway suspensions, passengers trapped in trains, and severe flooding in Miyun District, Beijing. (2) The evolution of topics of public attention strongly corresponds to the disaster process: From early warning and emergency risk avoidance, gradually move to disaster report and rescue coordination, and finally focus on the criticism of infrastructure vulnerability. (3) The emotional response presents phased characteristics. At the initial stage of the rainstorm red warning issued by the meteorological department, anxiety is dominant; after the release of rescue information, emotion rises briefly, and reflection and attribution tendencies generally appear after the disaster. (4) Elderly populations and those in remote areas exhibit characteristics of vulnerability, information isolation, and high dependency in response to disasters. Full article

We developed a hardware-in-the-loop simulation of a battery management system (BMS) using controller area network (CAN) as the communication backbone for electric-powered response boats in flood rescue. A LiFePO4 pack and discharge motor/charger were modeled in MATLAB/Simulink/Simscape, while an STM32 Nucleo-F446RE executed CAN messaging. The BMS monitored voltage, current, temperature, and state of charge. Results indicate CAN’s reliability under rescue-like disturbances: priority arbitration delivered over-temperature and over-current warnings ahead of routine telemetry; error detection and retransmission preserved data integrity; and bus-load analysis showed low latency for urgent frames without interrupting state-of-charge reporting, improving situational awareness and reducing operator risk. Full article

This paper developed a probabilistic framework for system level reliability and risk assessment that coupled hydraulic loading with structural response and explicitly modelled cascading interactions and statistical dependence between components. The contribution is a system-level reliability and risk modelling methodology that integrates dynamic cascading interactions, non-stationary design-life reliability accumulation, and system-level optimisation within a unified Monte Carlo architecture. Dynamic Monte Carlo simulation was used to evaluate individual, joint, conditional, and system-scale probabilities of failure across varying flood magnitudes and design lives. Model verification confirmed that discretisation and sampling errors were small relative to parameter-driven variability. Results showed that long-term system reliability arose from the combined influence of flood frequency, exposure duration, and the strength of interaction between interdependent structures. Frequent loading accelerates the accumulation of failure probability through repeated events, whereas rare events contribute more slowly but dominate extreme outcomes, indicating that cumulative reliability cannot be inferred by the linear extrapolation of annual probabilities. In an examined diversion–levee–basin configuration, strong structural coupling amplified vulnerability by contracting joint stability margins and increasing conditional failure probabilities. The system-level optimisation of structural parameters over the examined design life reduced cumulative system failure probability from 0.305 to 0.153, whereas single-component optimisation redistributed risk within the system without reducing total system risk. The framework advances beyond static risk analysis by integrating time-dependent reliability, cascading dependencies, and design-life optimisation for system-scale mitigation. Full article

Flood and flash flood events can generate severe hydraulic actions on civil structures, requiring modeling strategies able to link flow features to structural damage. This paper proposes a two-scale numerical framework based on advanced finite element modeling to assess the vulnerability of structures subjected to inundation and flood-driven impact. At the macroscale, the flood propagation and the interaction with the built environment are simulated through the depth-averaged Shallow Water Equations, adopting a time-explicit interface treatment to capture the evolution of the free surface. The macroscale model provides time-dependent water depth and flow velocity along the external surfaces of the structure, which are then used to derive hydrostatic and hydrodynamic actions, also in comparison with code-based formulations. At the mesoscale, these actions are transferred to a detailed structural model to investigate the nonlinear mechanical response of the building. Structural components are described through a coupled damage–plasticity constitutive law, enabling the prediction of stiffness degradation, cracking-driven damage patterns, and the identification of the most critical structural zones under flood loading. The proposed workflow is finally applied to a real structure located in the municipality of Cosenza (Italy), demonstrating the capability of the approach to combine hydraulic intensity measures with physics-based structural damage assessment, supporting scenario analyses and risk mitigation evaluations. Full article

Intensifying hydroclimatic extremes have heightened the need for basin-scale indicators of antecedent wetness that are relevant to flood responses. However, ground-based soil-moisture observations are spatially sparse, and satellite products frequently exhibit temporal gaps. To address this limitation, this study integrated satellite- and ground-soil-moisture observations, hydro-meteorological variables, and observed streamflow data from 2018 to 2024 across 26 standard basins (SBs) within three dam basin regions in South Korea: the Nam River Dam (NGD) and the upstream and downstream regions of the Seomjin River Dam (SJD). Using this integrated dataset, we quantified the relationships among precipitation, basin wetness, and rapid discharge increases, subsequently deriving composite thresholds for flood early warnings. For each SB, we trained a Random Forest regression model using satellite-soil-moisture and basin-representative hydro-meteorological inputs—including 1-day accumulated precipitation (P_1d), 7-day accumulated precipitation (P_7d), the antecedent precipitation index (API), and related meteorological variables—to estimate a continuous, daily basin-representative soil-moisture series (SM_RF). Validation results indicated that the coefficient of determination (R²) ranged from 0.6 to 0.7 for most SBs. Extreme event days were consistently associated with elevated values of SM_RF, P_1d, P_7d, and API, demonstrating that antecedent wetness significantly influences the likelihood of rapid discharge events. Finally, composite threshold scanning yielded candidate rules characterized by high precision, moderate hit rates, and low false-alarm rates, confirming the efficacy of the proposed framework for developing flash-flood early-warning thresholds in South Korean dam basins. Full article

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article