Search Results (480)

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions. Full article

This study addresses the dual challenges of seasonal water scarcity and urban flooding in the Garzan River basin, a region with a semi-arid climate. We propose and analyze an integrated water management system designed to mitigate these risks and promote both ecological and economic sustainability. Our methodology began with a comprehensive analysis of meteorological data from 2000 to 2024, which quantified the significant seasonal irregularity in the annual rainfall regime. The findings revealed that the bulk of the average 800 mm of rainfall occurs between January and May, while the summer months experience near-drought conditions. Based on this, we calculated the potential of various water conservation strategies. The system combines rainwater harvesting from a 1000 m² roof and a 500 m² parking lot, projected to collect 1020 m³ annually, with greywater reclamation and low-flow fixtures, which add a combined 400 m³ of annual savings. The total annual water savings of 1420 m³ were found to provide a gross annual economic benefit of $3550. Considering the installation and maintenance costs, the project’s payback period is estimated to be around 32 years. We also developed an annual precipitation prediction model providing a locally applicable early warning mechanism that forecasts total rainfall based on spring data. The use of proactive hydrometeorological data can improve the feasibility of long-term infrastructure projects to a certain extent. Finally, the proposed system’s design was confirmed to be eligible for multiple LEED certification credits, demonstrating its alignment with international sustainability standards. In conclusion, this research provides a comprehensive and viable solution that addresses local water issues and offers a valuable model for other regions facing similar challenges. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting. Full article

Under the background of global warming, high-tide floods pose growing threats to China’s coastal ecosystems, infrastructure and freshwater supplies. Although the occurrence of high-tide flooding events is widely recognized as being related to relative sea-level rise, tides, and residuals, the analysis and attribution of individual events remain relatively scarce. Based on tide-gauge data and numerical simulations, this study conducted a quantitative analysis of the high-tide flooding events along China’s southern coast around 19 November 2024 and provides mitigation recommendations. Results indicate that coastal sea levels south of the Yangtze River Estuary in November 2024 hit the third-highest November value on record. Sea levels south of the Taiwan Strait were 26 cm above those of normal years, with the highest monthly level since that recorded in 1980. Around 19 November, the coastal area levels coincided with the astronomical spring tide period, with the astronomical high water levels in Shanwei (Guangdong), Dongfang (Hainan), and Beihai (Guangxi) reaching 116 cm, 186 cm, and 292 cm above local mean sea level, respectively. Additionally, influenced by the outer circulation of the tropical cyclone Man-Yi and a cold-air process, an extensive coastal surge occurred, with 30~80 cm surges persisting for nearly 30 h. The combined effects of high sea levels, spring tides, and abnormal surges triggered extreme sea levels that broke historical records, with multiple stations reaching once-in-20-year levels. The contributions of astronomical tides to the extreme sea levels in Shanwei, Dongfang and Beihai were 49.5%, 69.3%, and 77.8%, respectively, while the contributions of surges were 23.6%, 6.4%, and 4.6%. This high-tide flooding event affected multiple coastal areas in Guangdong, Guangxi, and Hainan. Developing a comprehensive adaptation strategy encompassing emergency observation and early warning, risk assessment and zoning, coastal protection, coastal adaptive planning, and freshwater resources management is crucial for effectively addressing the risks of high-tide flooding events. Full article

Flash flood events are among the most critical hydrological hazards in arid and semi-arid regions, posing extreme threats to critical infrastructure, human safety, and sustainable development plans. This paper evaluates the flash flood susceptibility of the Al’Ataya catchment, a key watershed on the southern Red Sea coast, using an integrated geospatial analysis approach. To assess and quantify the flood hazard, we investigated 15 morphometric parameters for 24 particular sub-catchments within a sixth-order drainage system. Two complementary methods, the Morphometric Ranking Method and El-Shamy’s approach, were utilized to classify the catchment into different flood susceptibility levels. Results from the Ranking Method identified seven sub-catchments (SC-2, SC-3, SC-6, SC-7, SC-8, SC-9, and SC-19) as having high flood hazard levels, mainly driven by large watershed areas, steep slopes, and high relief ratios. In contrast, El-Shamy’s approach resulted in a different evaluation, identifying sub-catchments in Zone B (SC-23, SC-16, SC-17, SC-15, SC-6, SC-20) as high hazard sub-catchments due to the particular relationship between the bifurcation ratio parameter and the drainage density and stream frequency parameters. The integration of the two methods suggests that the susceptibility factor is controlled by the combined influence of a low drainage density and steep mountainous terrain draining toward the coastal zone. These results provide a spatial model for flood mitigation and early warning systems, supporting Saudi Vision 2030 through improvement to the development of southern urban centers such as Al’Ataya and Sabya. Full article

Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised concerns regarding its long-term stability and associated downstream flood hazards. Due to its geomorphological setting and potential exposure to multiple triggering mechanisms, including seismic activity and landslides, Lake Sarez is widely considered a high-consequence hazard system. Although the dam has remained stable for over a century and is currently monitored using modern geodetic and satellite-based technologies, uncertainties remain regarding its internal structure and response to extreme external forcing. While existing early warning systems enhance preparedness in downstream communities, effective long-term risk reduction requires continued monitoring, improved hazard modeling, and strengthened regional cooperation. This review synthesizes existing studies on the geological setting, hazard potential, stability assessments, and disaster risk management strategies related to Lake Sarez. It highlights the importance of integrated multi-hazard analysis and precautionary risk governance in managing low-probability but high-impact natural dam failure scenarios. Full article

Accurate short-term flood forecasting is vital for effective risk management and early warning systems. However, many data-driven models struggle to generalise with limited historical data and fail to consistently capture complex temporal dependencies across varying forecasting horizons. To address these challenges, this study proposes GHDFloodNet (Generalised Hybrid Data-limited Flood Prediction Network), a hybrid deep learning framework designed for robust multi-step-ahead forecasting. GHDFloodNet integrates First-Order Model-Agnostic Meta-Learning (FOMAML) with a Temporal Fusion Transformer (TFT) to enable rapid task adaptation and effectively capture long-range temporal dependencies and variable interactions. To further enhance predictive consistency, the framework incorporates a bidirectional Long Short-Term Memory (BiLSTM) network augmented with an additive attention mechanism and static feature fusion as a core learner within a meta-ensemble architecture. Bayesian hyperparameter optimisation within an AutoML framework identifies optimal model configurations, while a dedicated data handling layer with real-time augmentation improves stability under non-stationary conditions. The framework was evaluated for multi-horizon water level forecasting across four lead time ranges (1–6 h, 6–12 h, 12–24 h, and 24–48 h) using rainfall and lagged water level observations as primary inputs. Experimental results demonstrate that GHDFloodNet achieves robust, nearly invariant error distributions across the full 1–48 h forecast window, reporting an MSE of 0.53–0.55, RMSE of 0.72–0.74, and MAE of 0.35–0.36. Furthermore, the model exhibits stable goodness-of-fit, with R² and NSE values consistently ranging from 0.44 to 0.47 across all lead times, significantly outperforming conventional baselines, which typically exhibit pronounced error escalation at longer horizons. Overall, GHDFloodNet demonstrates that horizon-independent forecast reliability can be architecturally engineered, offering critical value for operational flood forecasting where consistent performance across all lead times outweighs peak short-range precision. Full article

Tropical mid-size cities on alluvial floodplains face compounded flood challenges combining pluvial accumulation from intense convective storms, regulated river overflow, and aging drainage networks. This study presents an integrated framework for Monteria, Colombia (~450,000 inhabitants; Sinu River, Caribbean lowlands), within Colombian Decree 1807/2014 and structured in four phases. (1) Hazard: A Rain-on-Grid 2D HEC-RAS 6.6 model covering 4090 ha, calibrated against four gauged events, identifies three dominant pluvial mechanisms (poor hydraulic connectivity, limited evacuation capacity, downstream channel overflow), plus 17 critical fluvial erosion points affecting ~289 properties at 100-year return period. (2) Vulnerability: Depth-damage functions from 1465 household surveys yield 36.36% of 3015 assets in high risk and 57.77% in medium risk. (3) Measures: Scenario M2 (channel widening plus dikes, land-raising, retention lagoons) removes 80 ha of flooding while displacing 28 ha at COP 845 million pre-design cost. Non-structural measures include a Sustainable Urban Drainage Master Plan, IoT-based Early Warning System, minimum construction-elevation map, and land-management instruments. A Monte Carlo residual-risk model reduces baseline risk to 19.9% under full implementation. (4) Emergency: A February 2026 cold-front event was addressed with a 4300 m perimeter dike and six pump stations deployed jointly by the Regional Environmental Authority (CVS) and Municipal Administration. Full article

Flood-induced scour remains one of the leading causes of bridge failure, yet the chain linking flood hazard to bridge decisions is still commonly treated as a sequence of disconnected tasks. This review examines that chain using uncertainty as a unifying interpretive framework, synthesizing the recent literature on non-stationary flood hazard assessment, bridge-scale hydraulics, scour processes and predictive models, scour monitoring, monitoring-informed forecasting, structural vulnerability, and risk-informed decision-making. The review synthesizes the state of the art across all these stages of the chain, highlighting how the dominant uncertainty changes along it: climate and hydrologic variability upstream; model-form, sediment, and parameter uncertainty in scour prediction; measurement noise and inverse-inference uncertainty in monitoring; and threshold and consequence uncertainty in closure, retrofit, and network-level decisions. Although major advances have been achieved in probabilistic modelling, machine learning, hybrid physics-informed methods, and multimodal sensing, most published frameworks still transfer deterministic outputs from one stage to the next. As a result, uncertainty is rarely propagated consistently to the decision level. The main value of this review lies in making the chain’s weak interfaces explicit, in showing how uncertainty propagation can serve as a unifying framework across otherwise disconnected literatures, and in identifying which methodological directions are most promising for connecting prediction, monitoring, and decision support into a coherent end-to-end probabilistic chain supporting climate-resilient bridge management. Full article

Convectively Coupled Kelvin Waves (CCKWs) play a key role in synoptic variability and can trigger extreme hydrometeorological events. This study characterizes the influence of CCKWs on seasonal precipitation patterns and extreme precipitation events (EPEs) over northern South America. Using a filtered OLR dataset, we found that precipitation anomalies associated with CCKWs are sensitive to the selected index region. A sensitivity analysis identified a region in the Colombian Pacific exhibiting the strongest precipitation anomalies linked to CCKWs. At seasonal scales, March–May (MAM) is the season with the highest CCKW activity, and its convective phase is associated with enhanced precipitation over the far eastern Pacific, western Amazonia, and northern Colombia, while suppressed convection dominates northwestern Brazil. In addition, three regions exhibit increases of up to 30% in EPE occurrence during convective-phase Kelvin waves: (i) the northwestern Amazon, (ii) northwestern Colombia, and (iii) the Peruvian coast. In contrast, EPE occurrence in the Colombian Pacific appears largely independent of CCKW passage, likely due to the strong background climatological rainfall in the region. We also analyze a flooding event in Turbo, Colombia, on 9 May 2007, that occurred during the passage of a convective-phase Kelvin wave and was preceded by days of enhanced low-level southwesterly flow convergence and persistent rainfall. Understanding the influence of these intraseasonal oscillations on precipitation and EPEs is essential for improving regional weather forecasts and supporting the development of early warning systems. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Flood hazards are intensifying across Africa due to rapid urban expansion and hydro-climatic variability. This study develops a multi-metric geospatial framework combining extreme value analysis, hydrograph-based metrics, and dependence modelling to quantify flood magnitude, frequency, timing, and joint risk dynamics. Daily precipitation and streamflow reanalysis data (1985–2025) were analyzed for two contrasting Tanzanian catchments: the large Rufiji basin (RU) and the smaller Mirongo catchment (MW). Annual maxima were modelled using the Generalized Extreme Value (GEV) distribution, complemented by flow duration curves, peak-over-threshold detection, and regression-copula dependence analysis. Results reveal strong hydrological contrasts. RU exhibits amplified rare-event growth (design floods from ~2850 to 11,770 m³/s), extended recession persistence (>100 days), low flashiness, and long rainfall-runoff lags (~15 days), indicating storage-dominated behavior. MW shows smaller design floods (~80 to 370 m³/s), higher flashiness, and short lags (~4 days), reflecting rapid, rainfall-driven response. Gaussian copula parameters indicate moderate dependence in both basins (0.32 and 0.34), suggesting that joint dependence alone does not distinguish flood mechanisms without complementary metrics. The proposed framework improves basin-specific flood risk profiling and supports geospatial early-warning system design in data-scarce Sub-Saharan environments. Full article

Greece and the eastern Mediterranean are among the regions that are most exposed to climate-driven natural hazards, with wildfires, floods, and extreme rainfall events consistently producing significant socioeconomic and environmental impacts. Although previous literature has addressed each hazard type individually, a systematic, comparative analysis of the atmospheric circulation regimes associated with all three hazard categories within a unified Lagrangian framework has not yet been conducted for Greece. In this study, a 96 h HYSPLIT back-trajectory analysis driven by ERA5 reanalysis data, combined with k-means clustering, is employed to characterise the air mass origins associated with extreme events in Greece from 2000 to 2020 at two atmospheric levels: 750 m and 3000 m above sea level. Wildfire events are predominantly linked to short-distance northeast airflow at 750 m, and are directly associated with the Etesian wind system and to a coherent northwest-west Mediterranean signal at 3000 m, reflecting the influence of the summer blocking anticyclone over Europe. Conversely, flood events are dominated by northerly flow at 750 m, driven by the eastern flank of Mediterranean depressions. These results indicate that flooding in Greece is primarily conditioned by surface cyclogenesis, regardless of the upper-level flow geometry. Extreme rainfall events exhibit the most complex structure, with a dominant upper-level cluster that describes a recurving trajectory consistent with cut-off low dynamics. Cross-hazard comparisons demonstrate that similar near-surface trajectory patterns may arise from different atmospheric drivers, underscoring the necessity of integrating Lagrangian trajectory classification with additional context, such as thermodynamic and seasonal, to enable robust multi-hazard attribution and enhance early warning capabilities in the eastern Mediterranean. Full article

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty. Full article