Search Results (1,740)

The adjoining parts of Northeast India represent one of the most seismically active regions of the Indian subcontinent. This region is classified as Zone V by the Indian Seismic Code, indicating maximum seismic hazard potential. This region has a history of several great earthquakes, including the 1897 Shillong and 1950 Assam events that resulted in widespread ground shaking, liquefaction, landslides and flooding damage. Hence, accurate probabilistic forecasts are essential for regional seismic hazard mitigation. An updated earthquake catalog was compiled for the area between 87–98° E and 20–30° N, with a focus on events of M_wg ≥ 5. A unified catalog was prepared for the analysis covering the period from 1897 to 2025. The region was divided into nine seismogenic zones to account for the spatial variability in recurrence patterns. The inter-event times between successive earthquakes were calculated for each zone, and three renewal models, lognormal, Weibull and gamma, were fitted by maximum likelihood estimation. The logarithmic probability of the likelihood function (ln L) was used to assess the model performance for a range of elapsed times (t) and forecast intervals (τ). The results indicate that the lognormal distribution at zero elapsed time (t = 0) provided the best fit and is, therefore, the most robust for short-term earthquake forecasting. Meanwhile, the Weibull model always gave the maximum of the conditional probabilities for longer elapsed times, especially for τ values between 13 and 25 years, and would, therefore, be preferable for long-term forecasts. Most of the zones had a gamma model that lay between the two, with moderate fits and probabilities. The zone analysis showed strong variability, with some zones with short recurrence times and high probabilities for the short term and others with long recurrence intervals. The Indo-Burma Range and the Eastern Himalaya zones had conditional probabilities of more than 90% for M_wg ≥ 5 earthquakes during 2044–2050, implying a higher seismic risk in these areas. The results suggest that there is no single renewal model that is optimal for all seismogenic zones and that the choice of model should be adapted to the elapsed time and the seismic characteristics of each zone. The present study offers a robust time-dependent probabilistic approach for the prediction of moderate-to-large earthquakes in Northeast India by combining an updated catalog and comparative renewal modeling. The findings are anticipated to contribute to seismic hazard assessment, infrastructure resilience planning and disaster preparedness strategies in one of the country’s most vulnerable regions. Full article

Over the past decade, the Punjab plains of Northern India have experienced recurrent flooding driven by hydroclimatic variability, specifically shifts in western disturbances that have intensified monsoon precipitation. Following a devastating flood in 2025, the region remains highly vulnerable to hydrological extremes, a risk further exacerbated by shifting land use and agricultural patterns, geomorphic parameters, and complex fluvial systems. This study assesses flood susceptibility by integrating multi-sensor satellite observations, multi-temporal Sentinel-1 backscatter signals, and refined runoff potential estimates derived from local climate zones, accounting for land cover, soil type, and infiltration characteristics, into machine learning frameworks. The model is trained using a 2025 flood inventory generated from a synthetic aperture radar backscatter threshold ratio. The calibrated frameworks are applied to the 2023 flood events to test independent transferability. The temporal consistency and predictive performance of the models are evaluated using the precision–recall trade-offs, threshold-dependent predicted probability distribution, and Shapley Additive exPlanations (SHAP). Results indicate more balanced classification performance of Random Forest and Extreme Gradient Boosting in comparison to Artificial Neural Network performance that exhibits higher recall with lower precision. The model performance for 2023 models is considered more robust, with greater class separability of 2023 flood events than for 2025. Probability distributions for both events further demonstrate model-dependent threshold behavior, highlighting a trade-off between flood detection sensitivity. SHAP identifies rainfall, soil moisture, runoff, and elevation as the dominant contributors. The analysis further indicates that all model frameworks effectively capture the physical control of hydrological and topographical variability on the temporal flood events. The consistent contribution of hydrological and topographical factors across the two events supports model transferability, while threshold sensitivity, uncertainty, and spatial dependence are important considerations for flood susceptibility modelling. The results reflect a balanced interaction between extreme rainfall, runoff potential, and topographic control in causing periodic floods in the Punjab plains. Full article

National energy planning increasingly needs to account for climate risks, yet adaptation priorities in National Energy and Climate Plans (NECPs) often remain too generic to guide infrastructure, regulatory, and investment decisions. This paper develops a policy-facing framework for translating energy sector climate risk evidence into ranked adaptation priorities. The framework decomposes the national energy system into supply chains and aggregate element types, combines stakeholder-derived system importance weights with multi-hazard exposure and vulnerability scores, and derives a relative impact ranking. Exposure is represented through hazard magnitude, frequency, and spatial prevalence, while vulnerability is assessed through expert-based sensitivity and adaptive capacity scores. The framework is demonstrated for Slovenia, covering electricity, gas, liquid fuels, solid fuels, and district heat as part of the country’s first national sectoral climate vulnerability and risk assessment for energy. The results identify electricity transmission and distribution networks, liquid fuel import and logistics infrastructure, gas imports and transmission, and selected storage and conversion assets as priority areas. In the RCP8.5 climate stress case, exposure rises substantially for floods, heat, landslides, and wildfires. Monte Carlo robustness checks indicate that the core set of high-priority element types remains stable. The paper contributes a system-wide screening approach for integrating climate risk knowledge into climate-resilient energy planning, adaptable to different national contexts. Full article

Before Cyclone Senyar made landfall on Sumatra in November 2025, it drew moisture from across the South China Sea, the Gulf of Thailand, and the Indian Ocean. The substantial damage its heavy rainfall caused on the NE and W coasts of Sumatra urges us to rethink the relationships between climate, forests, hydrology, land use, human presence, and vulnerability. Cyclones will recur, but flood damage does not have to be repeated. The simple narrative that “deforestation causes floods” is not adequate for guiding a “building back better” strategy. To account for the space-time pattern of Senyar effects with its multiple landfalls, we reviewed key concepts and framing of the links between ocean temperature, atmospheric moisture transport, rainfall extremes, saturation of existing buffers (“sponges”), river flow, and flooding. We hypothesize how atmospheric roughness slowing down rivers in the sky can induce congestion and precipitation. Surface infiltration and water retention in the soil profile matter. Mid- and downstream flow delays due to sponge effects depend on land cover beyond what a simple forest–non-forest terminology can represent. Rather than indiscriminate tree planting, adaptation efforts to avoid future disasters should balance reductions in human exposure (effective land use planning) and efforts to reduce hazards by restoring and managing vegetation and drainage systems. Full article

Climate change is intensifying challenges to microbial water quality and household water security across Saharan and Sahelian Africa, where water scarcity, variable infrastructure, and reliance on decentralized and informal water sources can increase vulnerability to contamination. This structured narrative review synthesizes evidence on how climatic stressors, including extreme heat, ultraviolet radiation, drought, flooding, rainfall variability, and dust events, influence microbial contamination, persistence, transport, and exposure across groundwater, surface waters, drinking-water distribution systems, and household storage. Heavy rainfall and flooding can mobilize fecal contamination and microbial hazards into water sources, whereas drought and water scarcity can increase dependence on marginal supplies, prolong household storage, and create conditions favorable to post-collection contamination and microbial regrowth. Climatic effects vary across microbial groups and water matrices: temperature, sunlight, turbidity, sediments, biofilms, and hydrological conditions can differentially influence bacterial, viral, and protozoan persistence and transport. Saharan dust also represents a potential pathway for long-range microbial dispersal and deposition, although direct evidence linking individual dust events to pathogen concentrations in drinking-water sources remains limited. The review further shows that microbial water quality is insufficiently integrated with broader dimensions of household water security, including availability, reliability, accessibility, and perceived safety. Strengthening the evidence base will require greater integration of microbial, climatic, hydrological, infrastructure, and household water-security data to support climate-resilient water management and public-health protection in Saharan and Sahelian communities. Full article

Underground subway stations are highly vulnerable to flooding due to their below-grade configuration, multiple interconnected levels, and reliance on a limited number of vertical access points, yet the extent to which flood progression affects evacuation performance remains insufficiently quantified. This study evaluates the impact of flooding on evacuation performance in an actual four-level transfer station in Seoul, South Korea, by coupling a hydraulic inundation model with a node–link-based evacuation simulation. Water depth and flow velocity obtained from an EPA SWMM-based inundation simulation were converted into walking-speed reduction factors and applied to link weights in a Dijkstra’s algorithm-based evacuation routing model, together with crowd-congestion-based speed reduction. Evacuation performance was compared across three scenarios: a non-flooded baseline and evacuation initiated under 300 s and 600 s flood conditions. The maximum evacuation time increased from 453 s under the non-flooded condition to 523 s and 596 s under the 300 s and 600 s flood conditions, respectively, corresponding to increases of approximately 15.5% and 31.6%. Tracing the route of the last evacuated group showed that this delay arose not from a change in route length—the governing route measured approximately 78 m in all three cases—but from a congestion-driven shift in which platform side produced the binding delay, as evacuee flow was redistributed away from the flooded exit toward the unaffected side. These findings indicate that the onset of flooding measurably increases evacuation time by altering the spatial distribution of congestion rather than the route itself, and that evacuation guidance based on a fixed, pre-computed route cannot fully offset this time-dependent penalty, underscoring the need for real-time, flood-adaptive evacuation routing in underground station environments. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

Urban flood control basins without forced drainage operate as one-shot systems: they must perform within a single event and cannot empty themselves until it ends. The timing of gate closure is therefore decisive—closing early wastes storage, whereas closing late lets stored water drain back to the falling channel—yet the recovery-based Reliability–Resiliency–Vulnerability (RRV) framework does not capture this trade-off. We reformulated the RRV indices for non-drainable basins and applied them to a full-scale experimental channel reproduced with high-resolution Structure-from-Motion terrain and simulated with the two-dimensional Nays2D Flood model. The three indices provide complementary performance dimensions: Reliability describes temporal threshold attainment, Resiliency describes event-integrated retention, and Vulnerability describes the maximum instantaneous deficit. Five gate-closing scenarios were compared with the no-gate reference. For this single-site, single-hydrograph event on the calibrated 211 × 111 grid, the net inflow at the inlet reversed at about t = 2611 s, and closures made before this instant avoided backflow. Closing just before reversal (t = 2574 s) gave the highest Resiliency (0.995), the lowest Vulnerability (0.074), and the largest net inflow (1487 m³) among the tested scenarios; delaying closure reduced Resiliency to 0.809 and raised Vulnerability to 0.255. The exact values are specific to the adopted grid and event, whereas the principal result is the reversal-based closure window. The method identifies this window using observed or forecast water level or flow direction information, without requiring new forced-drainage or pumping infrastructure. Full article

The phenomenon of climate change has exerted an escalating degree of pressure on coastal livelihoods in Bangladesh. In this region, communities find themselves confronted with a dual challenge, being exposed to both rapid-onset hazards, including cyclones, storm surges, and floods, and slow-onset stresses, such as salinity intrusion and sea-level rise. These hazards have ramifications for agriculture, aquaculture, food security, and livelihood sustainability. However, extant studies frequently examine individual risks, livelihood sectors, or disaster responses in isolation. Consequently, there is a paucity of research that comprehensively examines the influence of multiple climate-related hazards on coastal livelihoods and the adaptation options that are locally feasible in overcoming institutional, community, and financial constraints. This study synthesizes evidence from the published literature and is complemented by qualitative evidence from 30 open-ended questionnaires with farmers in selected coastal areas of Bangladesh. The objective of this synthesis is to validate and contextualize the current livelihood conditions, climate-related challenges, and locally adopted adaptation practices. The analysis examines four interconnected dimensions. The following elements are to be considered: (i) climate-related hazards and their sectoral impacts, (ii) livelihood opportunities and vulnerabilities, (iii) adaptation practices, and (iv) institutional, community, and financial constraints. These elements are to be complemented by a SWOT assessment of livelihood adaptation options. The findings indicate that a combination of factors, including salinity intrusion, cyclones, storm surges, flooding, sea-level rise, drought, and changing temperature and rainfall patterns, collectively impose constraints on agricultural and aquaculture productivity, thereby exacerbating livelihood insecurity. Concurrently, practices such as salt-tolerant crop cultivation, sorjan farming, freshwater conservation, pond-dyke vegetable cultivation, tower cultivation, composite agriculture, and environmentally responsible aquaculture offer promising pathways for livelihood adaptation. However, the limited availability of infrastructure, inadequate institutional coordination and stakeholder participation, and insufficient financial resources act as constraints on their broader adoption. The manuscript makes a similar observation regarding infrastructure, institutional support, and funding, which are identified as significant constraints. Concurrently, it acknowledges technological development, stakeholder participation, and climate-resilient livelihood opportunities as crucial avenues for enhancement. This study thus posits a series of recommendations for enhancing the resilience and sustainability of coastal livelihoods in Bangladesh. These recommendations encompass the implementation of hazard-specific and livelihood-oriented adaptation measures, the facilitation of access to climate-resilient technologies and financial resources, and the establishment of a more robust coordination framework among diverse stakeholders. The proposed framework involves collaboration between agriculture, fisheries, water management, disaster management, local government, non-governmental organizations (NGOs), academic institutions, and community organizations. These recommendations align with the manuscript’s proposed measures for communities dependent on salinity, cyclones, storm surges, floods, agriculture, and aquaculture. Additionally, the manuscript places emphasis on cross-sectoral governance. Full article

Sea-level rise threatens coastal ecosystems, infrastructure, and populations worldwide. This scoping review examined methods for assessing sea-level rise impacts in the MENA region, including their strengths, limitations, geographic distribution, and research gaps. Eligible studies were English-language journal articles or conference papers published between 2010 and 2026 that empirically assessed sea-level rise impacts within the MENA region using a defined methodological approach. Scopus and the Web of Science (WoS) Core Collection were searched using terms related to sea-level rise, assessment technologies, and MENA countries. A total of 407 records were identified, with 250 remaining after duplicate removal and preliminary exclusions. Following title and abstract screening, 110 reports underwent full-text evaluation, and 87 were ultimately included. Data were charted using a structured framework and analyzed descriptively and thematically. The studies were classified into remote sensing and Geographic Information Systems (GISs), index-based assessments, numerical modelling, and artificial intelligence (AI) and data-driven methods. Remote sensing and GISs were the dominant approaches, primarily applied to inundation, shoreline change, land subsidence, and exposure mapping, with their reliability influenced by elevation accuracy and simplified treatment of flood connectivity. Index-based approaches supported vulnerability screening, though their outputs were sensitive to indicator selection, weighting, and classification thresholds. Numerical models represented coastal processes and infrastructure performance more explicitly, although sparse observations and calibration data limited their applicability. AI and data-driven methods were used for predictive analysis and dynamic exposure estimation, but their reliability depended on training-data quality, independent validation, and physically grounded interpretation. Research was mostly concentrated in Egypt and Morocco, while substantial geographic gaps persisted across several MENA coastlines. Future research should integrate validated high-resolution observations, relative sea-level change, process-based modelling, socioeconomic projections, and independently validated data-driven methods to support more reliable coastal planning and adaptation. Full article

Flood events in agricultural floodplains reflect not only rainfall intensity but the vulnerability of the affected area at the moment of the event. This study examines the January–February 2026 flood in Larache Province, Morocco, through an integrated remote sensing workflow combining Sentinel-1 SAR, CHIRPS precipitation, and Dynamic World land cover in Google Earth Engine, processed via the rgee R package. Flood extent was mapped using SAR backscatter change detection and cross-checked against rainfall dynamics, yielding about 6660 ha of inundation (2.4% of the province), concentrated along the main Loukkos river corridor and adjacent floodplain around Ksar El Kebir, with smaller scattered patches to the south. Land cover was assessed across four temporal windows (spring 2025, pre-event, post-event, and spring 2026) to evaluate how the landscape entered the event and how it recovered. A significant share of normally cultivated land was in a bare-soil state before the flood, a condition that the literature associates with increased runoff. Post-flood analysis across 25 sample areas grouped into three geomorphic zones shows spatially uneven recovery, with cropland still below seasonal norms in spring 2026. These patterns are consistent with structural land-use conditions (wetland loss, intensive seasonal agriculture, drought-degraded soils) that may have amplified an already severe event. The findings support cover cropping, updated flood-hazard zoning, and targeted wetland restoration to reduce vulnerability in the Loukkos floodplain and comparable Mediterranean alluvial floodplains. Full article

The consequences of changing climate conditions and associated extreme weather are increasingly discussed in the global railway transport sector, as infrastructure damage and service disruptions are more widely reported. The Passenger Rail Agency of South Africa (PRASA) is a critical component of the national passenger rail service that provides safe, reliable and affordable rail services to millions of passengers annually. However, the increasing recorded frequencies of extreme weather events in the country means that PRASA’s operations and infrastructure are not exempt from these emerging hazards. This provided an opportunity for the study to assess the spatio-temporal exposure and climate risk of climate-related extreme weather events to PRASA infrastructure and operations. The study drew on the strengths of a mixed-methods approach, integrating quantitative and qualitative techniques. It combined recorded historical extreme-weather event data from the South African Weather Service (SAWS) with expert-based focus group discussions to address the research questions. Findings show an increase in the recorded frequency of extreme weather events over a period of 103 years (1920–2022) in both areas. These hazards indicate exposure that can lead to damage to physical rail infrastructure and to train service disruptions. In both areas, Kendall’s tau indicated significant positive monotonic trends in the frequency of several weather-related hazards over time. Focus group findings identified coastal flooding, flash floods and sea-level rise as the top priority climate-related risks for PRASA along other key infrastructure vulnerabilities. On this basis, the study recommends both rail infrastructure and operational adaptations. Full article

Northeast Thailand’s Chi-Mun River Basin is among Southeast Asia’s most flood-prone regions, experiencing annual monsoon inundation and extreme flood events that threaten community sustainability. Despite substantial investment in flood management infrastructure, community responses to flood hazards vary considerably, with some areas demonstrating adaptive capacity while others exhibit maladaptive development patterns that increase vulnerability. This study assesses community resilience to flood hazards by integrating flood exposure, environmental conditions (Sentinel-2 spectral indices), and population dynamics across 1157 locations in the Upper Chi River Basin, Maha Sarakham Province. A two-stage analytical framework combining K-means clustering and Random Forest classification identified five resilience classes: Slow Recovery (36.3%), Vulnerable Decline (25.8%), Unknown (17.7%), High Resilience (14.2%), and Maladaptive Growth (6.1%). Results reveal that Maladaptive Growth and High Resilience were clearly distinguished by population volatility (254.3 vs. 65.4), population change (+585.8% vs. −18.7%), and elevation (152.7 m vs. 168.1 m). Population trend emerged as the strongest predictor (importance = 0.135), followed by MNDWI (0.114) and elevation (0.113), indicating that demographic dynamics and topographic characteristics are more influential than flood frequency alone in determining resilience class membership. The findings reveal that Maladaptive Growth areas exhibit extreme population growth with high volatility in lower-elevation areas, whereas High Resilience communities maintain environmental quality and demographic stability despite population decline. These findings inform targeted interventions for sustainable flood risk management and contribute to understanding maladaptation in flood-prone regions, supporting the achievement of Sustainable Development Goals 11, 13, and 15. Full article

Climate change and urbanization are intensifying urban flood risks worldwide, making flood risk management critically important. While machine learning has been widely applied for flood susceptibility mapping, many studies do not integrate socioeconomic dimensions for comprehensive risk assessment; moreover, traditional subjective evaluation methods provide insufficient quantification of expert judgment uncertainty. This study presents a hybrid framework for urban flood risk mapping, which integrates interpretable machine learning and the Z-number-based Fuzzy Analytic Hierarchy Process (Z-FAHP), applied to Tokyo, Japan, utilizing a high-resolution digital elevation model (DEM) derived from airborne LiDAR and other publicly available geospatial datasets. The framework leverages machine learning efficiency while better accommodating vague linguistic expert judgments by incorporating confidence levels. Flood susceptibility was derived through machine learning with 14 features, with model performance rigorously evaluated using both non-spatial and spatial 4-fold cross-validation, and the CatBoost model demonstrated optimal performance. SHAP (SHapley Additive exPlanations) analysis was further employed to enhance model transparency by quantifying feature contributions. Exposure and vulnerability were quantified from 3 and 5 socioeconomic indicators, respectively, through Z-FAHP. These three criteria were synthesized to produce the flood risk map using simple additive weighting. Results reveal that approximately 32.9% of the study area faces high-to-very-high flood risk, concentrated in eastern lowlands and river corridors. This transferable framework can be applied to other cities globally as an effective tool for urban flood risk management. Full article

With increasing extreme rainfall, assessing whether emergency shelters remain accessible and functional during flood-constrained evacuation is essential for enhancing urban flood resilience. However, flood simulation, evacuation behavior, shelter suitability, and spatial accessibility are often examined separately, leaving the relationship between shelter reception performance and pre-disaster spatial support insufficiently clarified. This study evaluates emergency-shelter evacuation-support capacity in the Qingdao West Coast New Area under a 100-year, 24 h design rainstorm scenario. InfoWorks ICM was used to simulate two-dimensional surface inundation without explicitly resolving the sewer-pipe network, and hourly inundation depths were coupled with NetLogo to model evacuee movement, road passability, and shelter reception performance. An entropy-weighted TOPSIS model was applied within the 800 m road-network service areas of 20 shelters, comprising the 10 highest- and 10 lowest-arrival shelters. Results show that widespread shallow inundation coexisted with localized deep-water accumulation, and shelter reception performance varied substantially. Higher simulated arrivals did not necessarily indicate stronger evacuation-support capacity. Within the selected sample, parks and green spaces and intersection density received relatively higher weights. Four representative shelter cases were identified—core load-bearing, high-pressure risk, potential improvement, and inefficient vulnerable—providing a process-based basis for emergency shelter assessment and urban flood resilience enhancement. Full article