Search Results (472)

This study presents an economic risk evaluation of buildings in Samoa exposed to extreme sea level (ESL)-driven episodic flooding and permanent inundation from relative sea level (RSL) rise. A spatiotemporal risk analysis framework was applied at the building object level to calculate monetary loss, expressed as the exceedance probability loss (EPL) and average annual loss (AAL). Economic risk was enumerated at national and district levels between the period 2020 and 2140 based on RSL projections for medium confidence Shared Socioeconomic Pathways (SSPs). Over this century, national AAL for buildings from ESL flooding in 2020 is expected to double by 2100 (USD 47–51 million). Under high emissions scenarios SSP3-7.0 and SSP5-8.5, AAL rates decelerate after 2100 as permanent inundation loss increases. District level risk variability is evident. For example, Tuamasaga on Upolu Island accounted for 44% of national 100-year annual recurrence interval losses, while AAL for Aiga-i-le-Tai and Va’a-o-Fonoti over this century reaches 8% of total district building replacement values. Our model approach has potential future applications to evaluate spatiotemporal risk distribution for a broader range of socioeconomic impacts that may occur beyond directly affected flood inundation areas. Full article

Flooding poses a major hazard to rapidly urbanising cities in Southeast Asia, and risks are projected to intensify under climate change. Accurate risk assessment, however, is hindered by scarcity of hydrological and topographic data. Focusing on the Lower Prek Thnot River Basin, a peri-urban catchment of Phnom Penh, Cambodia, the study applied the Rainfall–Runoff–Inundation model and systematically augmented inputs: hourly satellite rainfall data, field-surveyed river cross-sections and representation of hydraulic infrastructure such as weirs and pumping. Validation used Sentinel-1 SAR-derived flood-extent maps for the October 2020 event. Scenario comparison shows that rainfall input and channel geometry act synergistically: omitting either degrades performance and spatial realism. The best configuration (Sim. 5) Accuracy = 0.891, Hit Ratio = 0.546 and True Ratio = 0.701 against Sentinel-1, and reproduced inundation upstream of weirs while reducing overestimation in urban districts through pumping emulation. At the study’s 500 m grid, updating land use from 2002 to 2020 had only a minor effect relative to rainfall, geometry and infrastructure. The results demonstrate that targeted data augmentation—combining satellite products, field surveys and operational infrastructure—can deliver robust inundation maps under data scarcity, supporting hazard mapping and resilience-oriented flood management in rapidly urbanising basins. Full article

With the increasing frequency of extreme rainfall events, pluvial flooding has become a critical challenge to the safety and sustainable development of megacities worldwide. This study proposes a multi-dimensional framework for assessing urban pluvial flood resilience (UPFR) by integrating a coupled hydrological-hydrodynamic model with system performance curves. The framework characterizes the dynamic evolution of resilience across three dimensions: rainfall characteristics, risk thresholds, and spatial scales. Results show that short-duration intense rainfall triggers instantaneous pipe overloading, whereas long-duration storms impose cumulative stress that leads to sustained systemic weakening, with the lowest resilience observed under extreme prolonged rainfall conditions. The specification of risk thresholds strongly influences resilience ranking, with the vehicle stalling risk (VSR) consistently showing the lowest resilience, followed by building inundation risk (BIR) and human instability risk (HIR). Spatially, pipes represent the weakest components, nodes maintain resilience under moderate stress, and the regional system exhibits a pattern of local weakness but overall stability, accompanied by delayed recovery. These findings highlight the importance of incorporating multi-threshold and multi-scale perspectives in flood resilience assessment and management. The proposed framework provides a scientific basis to support staged prevention measures and adaptive emergency response strategies, thereby enhancing urban flood resilience in megacities. Full article

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

As climate disasters intensify, their financial shockwaves increasingly threaten residential stability and the resilience of the U.S. mortgage market. While prior research links natural disasters to payment delinquency, far less is known about foreclosure—the terminal outcome of housing distress. We construct a novel property-level panel covering 55 flood, wildfire, and hurricane events, integrating transactional, mortgage, and insurance data. A difference-in-differences framework compares foreclosure rates for damaged parcels with nearby undamaged controls within narrowly defined hazard perimeters. Results show that flooding substantially increases foreclosure risk: inundated properties experience a 0.29-percentage-point rise in foreclosure likelihood within three years, with effects concentrated outside federally mandated flood-insurance zones. In contrast, wildfire and hurricane wind damage are associated with lower foreclosure incidence, likely reflecting standard insurance coverage and rapid post-event price recovery. These findings suggest that physical destruction alone does not drive credit distress; rather, insurance liquidity and post-disaster equity dynamics mediate outcomes. Policy interventions that expand flood insurance coverage, stabilize insurance markets, and embed climate metrics in mortgage underwriting could reduce systemic exposure. Absent such measures, climate-driven foreclosures could account for nearly 30% of lender losses by 2035, posing growing risks to both household wealth and financial stability. Full article

Climate change has intensified torrential rainfall and floods, causing frequent floodplain inundation with erosion and deposition. Large-scale waterfront facilities such as park golf courses are highly vulnerable, requiring systematic hydraulic safety evaluation. We simulated a recent flood in the Musim Stream using a two-dimensional FaSTMECH model to assess floodplain safety. The model showed excellent reproducibility (RMSE = 0.0176 m, NSE = 0.95 for depth; RMSE = 0.016 m/s, NSE = 0.87 for velocity). Flood risk indices—flood intensity (FI) and flood hazard rating (FHR)—and erosion–deposition indices—transient erosion and deposition index (TEDI) and steady erosion and deposition index (SEDI)—were applied. FI values were in the range of 0.3–6.4 (median 2.8) and FHR was in the range 0.7–10.2 (median 3.0), indicating that most floodplain areas exceeded the “high” to “extreme” risk range. TEDI was in the range of 0.004–4.15 (mean = 0.60), while SEDI was in the range of 0.001–5.59 (mean = 2.12). High TEDI values (0.6–0.9) occurred in curved and contracted sections, corresponding to observed erosion zones, whereas high SEDI values (0.8–1.0) were concentrated in the main channel. These results demonstrate that the indices effectively quantify and visualize floodplain risk, providing a practical basis for the design, placement, and maintenance of floodplain facilities. Full article

Urban coastal areas are increasingly vulnerable to compound flooding due to the convergence of extreme rainfall, storm surges, and infrastructure aging, especially in high-density settings. This study proposes and empirically validates a multi-scale strategy for enhancing urban flood resilience in the Macau Peninsula, a densely built coastal city with complex flood exposure patterns. Building on a previously developed network-based resilience assessment framework, the study integrates hydrodynamic simulation and complex network analysis to evaluate the effectiveness of targeted interventions, including segmented storm surge defense barriers, drainage infrastructure upgrades, and spatially optimized low-impact development (LID) measures. The Macau Peninsula was partitioned into multiple shoreline defense zones, each guided by context-specific design principles and functional zoning. Based on our previously developed flood simulation framework covering extreme rainfall, storm surge, and compound events in high-density coastal zones, this study validates resilience strategies that achieve significant reductions in inundation extent, water depth, and recession time. Additionally, the network-based resilience index showed marked improvement in system connectivity and recovery efficiency, particularly under compound hazard conditions. The findings highlight the value of integrating spatial planning, ecological infrastructure, and systemic modeling to inform adaptive flood resilience strategies in compact coastal cities. The framework developed offers transferable insights for other urban regions confronting escalating hydrometeorological risks under climate change. Full article

Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards. Full article

Flood risk in small streams is rising under climate change, as small catchments are highly vulnerable to short, intense storms. We develop a high-resolution assessment that integrates a Digital Surface Model (DSM), a Digital Elevation Model (DEM), and airborne LiDAR within a MATLAB (2025b) hydraulic workflow. A hybrid elevation model uses the DEM as baseline and selectively retains DSM-derived structures (levees, bridges, embankments), while filtering vegetation via DSM–DEM differencing with a 1.0 m threshold and a 2-pixel kernel. We simulate 10-, 30-, 50-, 100-, and 200-year return periods and calibrate the 200-year case to the July 2025 Sancheong event (793.5 mm over 105 h; peak 100 mm h⁻¹). The hybrid approach improves predictions over DEM-only runs, capturing localized depth increases of 1.5–2.0 m behind embankments and reducing false positives in vegetated areas by 12–18% relative to raw DSM use. Multi-frequency maps show progressive expansion of inundation; in the 100-year scenario, 68% of the inundated area exceeds 2.0 m depth, while 0–1.0 m zones comprise only 13% of the footprint. Unlike previous DSM–DEM studies, this work introduces a selective integration approach that distinguishes structural and vegetative features to improve the physical realism of small-stream flood modeling. This transferable framework supports climate adaptation, emergency response planning, and sustainable watershed management in small-stream basins. Full article

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

Costa Rica has experienced 45 tsunamis at both its Pacific and Caribbean coasts, with none to moderated impact. However, the coastal population has increased exponentially in the past few decades, which might lead to higher impact in future tsunamis. In 2018 and 2019, IOC/UNESCO organized Experts Meetings of Tsunami Sources, Hazards, Risks and Uncertainties associated with the Tonga–Kermadec and Colombia–Ecuador subduction zones, where experts defined maximum credible scenarios. Here we modeled the propagation of those tsunami scenarios to Costa Rica and their inundation for selected sites. We found that the Tonga–Kermadec scenarios provoked more inundation than previous modeled sources from that region. However, the large travel time for those scenarios, about 14 h, would allow for a timely evacuation. In the Colombia–Ecuador scenarios, they provoked less inundation than previously modeled sources from that region, a good outcome as their arrival time is between 75 and 150 min. These new results required the update of tsunami evacuation maps and/or plans for many communities but provided more favorable conditions for tsunami preparedness. Yet, the short arrival times of the Colombia–Ecuador scenarios still require a prompt response from the population and authorities. For this, additional to updated tsunami evacuation maps and plans, it is recommended to have tsunami exercises on a regular basis. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article