Search Results (1,018)

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning. Full article

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

The city of Uvira, located in the eastern Democratic Republic of Congo (DRC), is increasingly experiencing flood events with devastating impacts on human life, infrastructure, and livelihoods. This study evaluates flood susceptibility in Uvira using Geographic Information Systems (GISs), and an Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Making approach. It integrates eight factors contributing to flood occurrence: distance from water bodies, elevation, slope, rainfall intensity, drainage density, soil type, topographic wetness index, and land use/land cover. The results indicate that proximity to water bodies, drainage density and slope are the most influential factors driving flood susceptibility in Uvira. Approximately 87.3% of the city’s land area is classified as having high to very high flood susceptibility, with the most affected zones concentrated along major rivers and the shoreline of Lake Tanganyika. The reliability of the AHP-derived weights is validated by a consistency ratio of 0.008, which falls below the acceptable threshold of 0.1. This research provides valuable insights to support urban planning and inform flood management strategies. Full article

Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019–2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources. Full article

Coastal climate processes that affect landscape hydrology and beach dynamics are studied using local and remote data sets near Cape Vidal (28.12° S, 32.55° E). The sporadic intra-seasonal pulsing of coastal runoff, vegetation, and winds is analyzed to understand sediment inputs and transport by near-shore wind-waves and currents. River-borne sediments, eroded coral substrates, and reworked beach sand are mobilized by frequent storms. Surf-zone currents ~0.4 m/s instill the northward transport of ~6 10⁵ kg/yr/m. An analysis of the mean annual cycle over the period of 1997–2024 indicates a crest of rainfall over the Umfolozi catchment during summer (Oct–Mar), whereas coastal suspended sediment, based on satellite red-band reflectivity, rises in winter (Apr–Sep) due to a deeper mixed layer and larger northward wave heights. Sediment input to the beaches near Cape Vidal exhibit a 3–6-year cycle of southeasterly waves and rainy weather associated with cool La Nina tropical sea temperatures. Beachfront sand dunes are wind-swept and release sediment at ~10³ m³/yr/m, which builds tall back-dunes and helps replenish the shoreline, especially during anticyclonic dry spells. A wind event in Nov 2018 is analyzed to quantify aeolian transport, and a flood in Jan–Feb 2025 is studied for river plumes that meet with stormy seas. Management efforts to limit development and recreational access have contributed to a sustainable coastal environment despite rising tides and inland temperatures. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

To investigate the energy conduction behavior of polymer gel profile control and flooding in low-permeability reservoirs, a parallel dual-tube displacement experiment was conducted to simulate reservoirs with different permeability ratios. Injection schemes included constant rates from 0.40 to 1.20 mL/min and dynamic injection from 1.20 to 0.40 mL/min. Pressure monitoring and shunt analysis were used to evaluate profile control and recovery performance. The results show that polymer gel preferentially enters high-permeability layers, transmitting pressure more rapidly than in low-permeability zones. At 1.20 mL/min, pressure onset at 90 cm in the high-permeability layer occurs earlier than in the low-permeability layer. Higher injection rates accelerate pressure buildup. At 0.80 mL/min, permeability contrast is minimized, achieving a 22.96% recovery rate in low-permeability layers. The combination effect of 1.2–0.4 mL/min is the best in dynamic injection, with the difference in shunt ratio of 9.6% and the recovery rate of low permeability layer increased to 31.23%. Polymer gel improves oil recovery by blocking high-permeability channels, expanding the swept volume, and utilizing viscoelastic properties. Full article

The construction of a dam significantly alters downstream flow characteristics, often analyzed through changes in flow–duration curves before and after construction. Typically, post-dam flow–duration curves exhibit increased probabilities in low-flow zones and decreased probabilities in high-flow zones, primarily influenced by reservoir operation methods (ROMs). This study introduces a Bayesian framework to replace ROM simulations for predicting downstream flow–duration curve changes after dam construction, mainly during the flood season. Within this framework, inflow data are treated as random variables, and the ROM is analogized to a likelihood function in Bayesian analysis. The key challenge lies in deriving a likelihood function that mimics the given ROM. The Rigid ROM, a hybrid of constant rate and constant magnitude ROMs commonly used in the Republic of Korea, is targeted in this study. Using hourly inflow data from the Republic of Korea’s Andong Dam (2010–2019), the proposed Bayesian method produces flow–duration curves closely matching simulation-based results, validating its accuracy. Furthermore, the method’s ability to seamlessly handle multi-dam systems in a series highlights its practical advantage, attributed to the iterative nature of Bayesian updates. This study underscores the Bayesian approach’s potential for efficient and robust flow–duration curve modeling in complex hydrological systems. Full article

Increasing frequency and severity of urban flooding, driven by climate change and urban population growth, present major challenges. Traditional flood control infrastructure alone cannot fully prevent flood damage, highlighting the need for a comprehensive and multi-dimensional disaster management approach. This study proposes the Flood Risk Index for Building (FRIB)—a building-level assessment framework that integrates vulnerability, hazard, and exposure. FRIB assigns customized risk levels to individual buildings and evaluates the effectiveness of a multi-dimensional method. Compared to traditional indicators like flood depth, FRIB more accurately identifies high-risk areas by incorporating diverse risk factors. It also enables efficient resource allocation by excluding low-risk buildings, focusing efforts on high-risk zones. For example, in a case where 5124 buildings were targeted based on 1 m flood depth, applying FRIB excluded 24 buildings with “low” risk and up to 530 with “high” risk, reducing unnecessary interventions. Moreover, quantitative metrics like entropy and variance showed that as FRIB levels rise, flood depth distributions become more balanced—demonstrating that depth alone does not determine risk. In conclusion, while qualitative labels such as “very low” to “very high” aid intuitive understanding, FRIB’s quantitative, multi-dimensional approach enhances precision in urban flood management. Future research may expand FRIB’s application to varied regions, supporting tailored flood response strategies. Full article

This study addresses the unclear mechanisms by which preferential flow channels (PFCs), formed during long-term waterflooding, affect nano-gel microsphere (NGM) flooding efficiency, utilizing CMG reservoir numerical simulation software. A dynamic evolution model of PFCs was established by coupling CROCKTAB (stress–porosity hysteresis) and CROCKTABW (water saturation-driven permeability evolution), and the deep flooding mechanism of NGMs (based on their gel properties such as swelling, elastic deformation, and adsorption, and characterized by a “plugging-migration-replugging” process) was integrated. The results demonstrate that neglecting PFCs overestimates recovery by 8.7%, while NGMs reduce permeability by 33% (from 12 to 8 mD) in high-conductivity zones via “bridge-plug-filter cake” structures, diverting flow to low-permeability layers (+33% permeability, from 4.5 to 6 mD). Field application in a Chang 6 tight reservoir (permeability variation coefficient 0.82) confirms a >10-year effective period with 0.84% incremental recovery (from 7.31% to 8.15%) and favorable economics (ROI ≈ 10:1), providing a theoretical and engineering framework for gel-based conformance control in analogous reservoirs. Full article

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m³/s, 60.37 m³/s, 41.40 m³/s, 38.33 m³/s, and 18.72 m³/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions. Full article

Users of technical blades expect new generations of tools to feature reduced power requirements for process and maximized tool life. The second aspect is reflected in the reduction in costs associated with the purchase of tools and in the reduction in process line downtime due to tool replacement. Meeting these demands is particularly challenging in cutting operations involving heterogeneous materials, especially when the processed raw material contains inclusions and impurities significantly harder than the material itself. This situation occurs, among others, during flatfish skinning operations analyzed in this paper, a common process in the fish processing industry. These fish, due to their natural living environment and behavior, contain a significant proportion of hard inclusions and impurities (shell fragments, sand grains) embedded in their skin. Contact between the tool and hard inclusions causes deformation, wrapping, crushing, and even chipping of the cutting edge of planar knives, resulting in non-uniform blade wear, which manifests as areas of uncut skin on the fish fillet. This necessitates frequent tool changes, resulting in higher tooling costs and longer operating times. This study provides a unique opportunity to review the results of in-service pre-implementation tests of planar knives in the skinning operation conducted under industrial conditions. The main objective was to verify positive laboratory research results regarding the extension of technical blade tool life through optimization of sharpening conditions during grinding. Durability test results are presented for the skinning process of fillets from plaice (Pleuronectes platessa) and flounder (Platichthys flesus). The study also examined the effect of varying cooling and lubrication conditions in the grinding zone on the tool life of technical planar blades. Sharpening knives under flood cooling conditions and using the hybrid method (combining minimum quantity lubrication and cold compressed air) increased their service life in the plaice skinning process (Pleuronectes platessa) by 12.39% and 8.85%, respectively. The increase in effective working time of knives during flounder (Platichthys flesus) skinning was even greater, reaching 17.7% and 16.3% for the flood cooling and hybrid methods, respectively. Full article